Donald Doherty's blog

Tag: Brain Science

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

    The Neuroscience Information Framework (NIF) website has become the hub for accessing every type of neuroscience resource from raw data to computer software and research papers. There are two big reasons to make NIF your neuroscience central. First, NIF utilizes the most advanced Web-based informatics technologies available including the Semantic Web so that you may efficiently find what you’re looking for. Second, NIF provides access to resources not typically indexed by search engines so that you’re provided with the most comprehensive Web portal to neuroscience resources available.

    Figure 1. The NIF home page with the words “EphB2 Alzheimer’s disease” typed into the NIF search box.

    In fact, NIF provides so much that it can be overwhelming if you begin by trying to read all of the verbiage about what they provide. As a first step I recommend diving in by using the NIF search box prominently displayed towards the top of the page under “Search for Neuroscience Resources.” For instance, type “EphB2 Alzheimer’s disease” into the search box like in Figure 1 above and press your enter (or return) key or click on the search icon to the right of the box to initiate the NIF search.

    Figure 2. The page displayed immediately after the search in Figure 1 was entered.

    A page like the one shown in Figure 2 above should appear. This is a long way from a simple list of matching resources! It can look complicated. However, NIF provides access to a lot of highly focused returned resources in a very logical manner. Let’s take a closer look.

    At the top left of the page you should see the “Search the NIF” heading. Under this you should see a search box with the words we entered “EphB2 Alzheimer’s disease” displayed. Under the search box you’re provided options to AND the terms you entered (the default) or to OR the terms. This is followed by a “View/Edit Query” heading, which provides a detailed view of the search construct created and used by NIF based on the search terms you entered. NIF provides you a lot of power by enabling you to modify the “under the hood” query construct if you so desire. Let’s skip this. NIF provides additional power over your search in the panel to the right under the “Search Options” heading. This presents synonyms from NIF’s extensive ontologies. We’ll also skip this for now but it’s good to know you have all of this power available to you.

    Note: NIF provides extensive tutorials (including videos) on a lot of its functionality.

    Notice the tabs across the top of the next panel down. You should see four tabs including Data Federation, NIF Registry, Literature, and Grants. Each tab heading is followed by the number of hits from your search (in parentheses) within the category. In our case we got 18 hits in Data Federation, which are data sources, and 68 hits in literature.

    Note: NIF continues working on your query even after the search results page is displayed. For instance, even though NIF initially returned 68 hits in literature that continued to be added to until it grew to 4,396 hits in literature.

    The Data Federation tab displays two categories for the data in our search: Data Type and Nervous System Level. Under the Data Type category, which list experimental data types, you’ll find the Data Type subcategory Microarray. The number associated with Microarray shows you the number of microarray data sets available, which is 18 in this case. Finally, under the Microarray subcategory is listed the data source of which there is only one in this case. All 18 microarray data sets are under Gemma: Microarray which are available through the Gemma online data repository. The other category, Nervous System Level, provides a biology-centric list of available data. In this case NIF has found 18 gene resources associated with the nervous system. The gene resources are all represented by 18 sets of microarray data from the Gemma online data repository. The 18 gene resources are not necessarily different genes. In fact, in our current search they’re all the same EphB2 gene. Each listing does represent a unique gene resource, which in this case is a unique microarray data set from various tissues all from human subjects. You can see this very quickly by scanning the data available to you in the area to the right of the Categories listings. Notice also the ever present availability of tutoring resources (far right).

    This has been a brief look at a tiny piece of what NIF provides. NIF is the place to start when looking for neuroscience resources.

    Note: Please let me know if you’re interested in hearing more about NIF.

  • Brain Scans (fMRI) on Stuttering Song Birds

    Vibrantly colored zebra finch males sing to attract a mate. Females display a more muted plumage and do not sing. These difference between the sexes have inspired a great deal of sexual dimorphism research in the species. In the embryo the male zebra finch produces estrogen which is transformed into a testosterone-like hormone in the brain. This difference leads to the development of a song system in the male nervous system but not in the female. Even with the song system in place the male zebra finch must learn to sing. The young bird works on matching his singing to the memory of his father’s song. Most males will sing their father’s song with little variation but they may also incorporate sounds and songs by other males they come in contact with. Because of these attributes (and others) zebra finch males have played an important part in research on learning, memory, and development.

    Recent studies have shown that a small fraction (about 7%) of laboratory raised male zebra finches stutter. Stuttering consists of a variant of the birdsong that contains three or more successive repetitions of song syllables, usually the last syllable in a sequence. About half of juvenile male zebra finches learn to reproduce the stuttering when they are tutored by a stutterer whereas the other half resist stuttering despite having a stuttering tutor. The authors of a recent paper (“Altered Auditory BOLD Response to Conspecific Birdsong in Zebra Finches with Stuttered Syllables” published December 23, 2010 in PLoS ONE) asked if there is any alteration in sensory song representation in the brains of birds that stutter.

    To investigate, the team used functional magnetic resonance imaging (fMRI) on awake zebra finches presented with songs. All of the subject birds where normal but were tutored by stuttering birds. The tutored subjects fell into two groups; those who mimicked the stuttering and those who did not stutter. They found that the pupils who stuttered showed significantly reduced responses to their tutor’s song and enhanced responses to unfamiliar songs. The results are the first evidence for a neural correlate of song representation in birds that, after learning songs from a stuttering tutor, have a tendency to stutter themselves. It will be interesting to see if stuttering zebra finches can help provide insights into the neurodevelopmental basis of stuttering in general.

    Other related blog posts:

    How Do Brain Circuits Generate Complex Sequential Behaviors?

  • Spontaneously Formed Neuronal Groups Far Exceed the Number of Neurons

    A little over a month ago we took a peak at a 2004 paper from Dr. Gerald Edelman’s laboratory that updated Dr. Edelman’s group selection theory by including axon conductance delays and spike timing dependent plasticity (STDP) in a massive computer model of the cerebral cortex containing 100,000 neurons and 8.5 million synaptic connections. The first author of that paper, Dr. Eugene Izhikevich, published a new paper in 2006 (“Polychronization: Computation with Spikes” published February 2006 in Neural Computation) that focused on the remarkable properties that emerged from the addition of axon conductance delays and STDP in a highly simplified model of cerebral cortex containing 1,000 neurons. This is probably the key paper describing the author’s results and ideas surrounding what he calls “polychronization” (poly means many and chronous means time) or the spontaneous formation of neuronal groups defined as “small collectives of neurons having strong connections with matching conduction delays and exhibiting time-locked but not necessarily synchronous spiking activity” (they may fire at many different times).

    Note: An appendix in the paper provides a MATLAB version of the model. In addition, MATLAB and C++ versions of the “Polychronization: Computation With Spikes (Izhikevich 2005)” model are available at SenseLab’s ModelDB.

    The sparse network (0.1 probability of connection between any two neurons) of 1000 randomly connected spiking neurons included axon conductance delays and STDP. This relatively simple network composed of 80% excitatory and 20% inhibitory neurons displayed dynamics similar to those seen in the mammalian cerebral cortex including 4 Hertz delta oscillations, 40 Hertz gamma oscillations, and balanced excitation and inhibition. An important finding reported in this paper is that the number of coexisting polychronous groups may far exceed the number of neurons in the network. In other words, these highly dynamic and spontaneously formed groups have the potential to carry a huge amount of information.

    Other related blog posts:

    Neuronal Group Selection and Spike Timing Dependent Plasticity

    Dynamical Systems and Silicon Based Hybrid Spiking Neurons