Donald Doherty's blog

Tag: Semantic Web

  • Sex Matters But the Brain is Like Nothing Else

    My longtime friend and mentor Dr. Karl Pribram has often said to me that anything may be found in the brain. It seems that whatever the current trend is – hydraulics or computers, chaos theory or graph theory – some structure or function in the brain supports the idea in some way. It’s only a matter of time when quantum and/or optical hybrid computing machines are common and a larger audience will understand and perhaps embrace Karl’s holonomic hypothesis.

    Figure 1. The construction of a cortical anatomical network by diffusion tensor imaging. See text below for more information. From “Sex- and Brain Size–Related Small-World Structural Cortical Networks in Young Adults: A DTI Tractography Study“. By Chaogan Yan, Gaolang Gong, Jinhui Wang, Deyi Wang, Dongqiang Liu, Chaozhe Zhu, Zhang J. Chen, Alan Evans, Yufeng Zang and Yong He. Cerebral Cortex Volume 21, Number 2, February 2011.

    A currently popular topic is graph theory, which is certainly dear to my heart as core to Semantic Web technologies and forms the mathematical foundation of many other networking technologies and analyses. Naturally, graph theory is currently being used while investigating many different things including the central nervous system.

    The authors of the new paper “Sex- and Brain Size–Related Small-World Structural Cortical Networks in Young Adults: A DTI Tractography Study” (published February 2011 in Cerebral Cortex) hypothesized that there are sex and brain size related differences in the patterns of anatomical connectivity in the human brain. To test their hypothesis, the research team used diffusion tensor imaging (DTI) techniques on 72 healthy young human adults to construct interregional connectivity for each participant and calculate topological parameters using graph-theoretical approaches. They then investigated the association of interregional connectivity and topological parameters with sex and brain size.

    The research team derived the cerebral cortical network connectivity of each individual using a multi-stepped process, which is outlined visually in Figure 1 above. The structural image of a brain was first transformed into what is called diffusion tensor imaging native space (a). Next the image was segmented into grey matter, white matter, and cerebrospinal fluid (b; left image). The automated anatomical labeling template (b; right image) was applied to an individual’s segmented diffusion tensor imaging native space brain image (c). In parallel, each individual’s white matter fibers were reconstructed in the whole brain by using diffusion tensor imaging deterministic tractography (d).

    The result is a brain mapped into 39 cerebral cortical regions within each hemisphere and associated with a connection matrix weighted according to the number of fibers connecting the regions. At bottom (e) left is a connection matrix color coded for the fiber density between each pair of regions. At bottom middle and right are views of the brain showing connections as graphs with nodes placed at the center of mass for each of the 39 brain regions per hemisphere. Edge thicknesses are coded according to the number of fibers connecting the regions.

    Consistent with previous studies, the research team found that women’s brains were significantly smaller than those of men. The team found that the difference in brain size between males and females remained after correcting for height. Unfortunately, they didn’t report if the difference remains significant after correcting for overall body mass. Also consistent with previous research results is their finding that the anatomical networks of the human brain have “small-world network” characteristics with relatively greater local interconnectivity and an emphasis on the shorter connections between regions.

    This study goes beyond earlier studies by showing that females have greater local clustering in cortical anatomical networks as compared with males, suggesting higher local network efficiency in the female brain. They also found that brain size is significantly and negatively correlated with local clustering, suggesting that smaller human brains are more efficient in local information transfer. Interestingly, they found that the brain size effect on local efficiency was not significant in males.

    The brain is like many things. Probably because the brain is really completely unlike anything humans have ever built or understood. Put another way, Newtonian physics is an approximation (it’s “like”) relativity physics within a particular scale of time and space. The brain encompasses so much structural and functional complexity that there are a lot of things “like” in the brain. It is fun and sometimes even useful to say that a certain system in the brain is like a telephone switchboard, a computer, or a social network. However, we must keep in mind that the brain is really not the same as anything else we know.

  • NIF: Better Literature Search

    Over the past couple of days we’ve looked at the data aggregating capabilities of the Neuroscience Information Framework (NIF). What about literature search? Is there any reason you should move from PubMed or Google Scholar to NIF?

    Figure 1. NIF literature search results for “barrel cortex.” NIF provides four tabs on the literature search results page.

    NIF provides the results of a standard PubMed search under the PubMed tab. In addition, NIF provides Open Access Literature, Neuronal Morphology, and Neuroscience Literature tabs. The Open Access Literature tab provides a convenient way to go directly to relevant articles that are freely available over the Internet. The Neuronal Morphology tab provides quick access to papers associated with digitally reconstructed neurons available through NeuroMorpho.org. The Neuroscience Literature tab lists papers returned based on a full text search using your search words. This could arguably the most valuable additional literature search service that NIF provides.

    Unfortunately the majority of papers are still published in journals that are not open access. Because of legal restrictions, NIF is only able to provide full text search services on a subset of papers that include those published in open access journals and those published in the Journal of Neuroscience. NIF provides a complete list of journals searched here.

    There is a large literature surrounding the amyloid beta protein. Recent evidence suggests that amyloid beta 42 is particularly important in Alzheimer’s disease. What if we only want papers that used amyloid beta 42 in their research? I decided to run a quick test of the full text search capabilities by typing the following “amyloid beta 42” into the NIF home page search box and clicking on the search icon. That didn’t even kick off a search but took me to a blank NEUROLEX page (under neurons and brain regions). The same thing happened when I typed in “amyloid-beta 42” but “abeta42” worked.

    Note: All of the variants above worked when I typed them into the search box returned by the initial search from home page (like at top left in Figure 1 above). Surprisingly, searches for “amyloid beta 42” and “amyloid-beta 42” each resulted in zero hits under “Neuroscience Literature.”

    A search for “abeta42” returned 161 papers under Neuroscience Literature. I checked all the articles in the first three pages and the last page and they all included abeta42 in the title or the abstract so I’m not sure if we’ve located additional articles that, for example, may have only mentioned the 42 amino acid peptide in its methods section. Also, it doesn’t look like synonyms are being used in the search.

    Other related blog posts:

    NIF: When You’re Looking for Neuroscience Resources Including Data

    NIF: Neurons, Models, and Grants

  • NIF: Neurons, Models, and Grants

    Yesterday we took an initial peek at the Neuroscience Information Framework (NIF). Today I’d like to briefly point to the range of Data Federation resources you can find there. When “barrel cortex” is typed into the NIF search box and a search is executed, a couple of subcategories come up under the Nervous System Level category and about five subcategories under Data Type like in Figure 1 below.

    Figure 1. NIF search results for “barrel cortex.” The search words refer to whisker related structures in layer 4 of somatosensory cortex.

    In Figure 1 above, the NeuroMorpho: NeuronInfo entry under the Cellular Level subcategory is selected which results in showing a list of some of the 66 neurons from the NeuroMorpho.org repository that are associated with the barrel cortex.

    Figure 2. A section from a mouse brain in BrainMaps.org. The barrel cortex is labeled “barrels” in red with yellow background.

    Click on the plus (+) sign to the right of Brain Regions to display entries in that subcategory. The BAMS: BrainRegions entry is associated with an anatomy ontology that provides standard naming conventions for brain structures. The BrainMaps:Atlas entry provides access to relevant high-resolution histology sections. Click on the barrel cortex, barrels link under Brain Region and the BrainMaps.org atlas will appear like in Figure 2 above.

    Under the Data Type category, the Images subcategory lists the sites we’ve already looked at. The Models subcategory lists computational model sites relevant to our search. The ModelDB: Models entry lists three models of barrel cortex in the SenseLab ModelDB repository.

    You may be puzzled why the Grants subcategory under Data Type shows large number of hits (73 while writing this) but the Grants tab at the top of the section has none. The Grants subcategory provides access to resources on past and currently funded grants. In contrast, the Grants tab provides resources for those wanting to apply for federally funded grants.

    Yesterday we looked at resources available under the Microarray subcategory. Our current search brought up 1 resource at the Gene Expression Omnibus (GEO). Finally, under Connectivity you will find information on the known neural connections to and from the brain area or structure you searched on. Tomorrow we’ll take a look at what the NIF Literature tab provides.

    Other related blog posts:

    NIF: When You’re Looking for Neuroscience Resources Including Data