Tag: Semantic Web

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.

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.

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.

In my blog post “NEURON, SenseLab ModelDB, NeuroMorpho.org, and Signal Processing in Brain Microcircuitry” the computational neuroscience paper under review “Local Control of Postinhibitory Rebound Spiking in CA1 Pyramidal Neuron Dendrites” (published May 5, 2010 in the Journal of Neuroscience) referenced two NEURON models in the ModelDB online data repository. We looked at the model directly associated with the paper in the blog post “A Taste of Neuroscience Papers in the Future.” In this post we’ll look at the foundational model of hippocampus CA1 pyramidal cells (and associated paper) that they referenced.

The paper “Signal Propagation in Oblique Dendrites of CA1 Pyramidal Cells” (published December 2005 in the Journal of Neurophysiology) reports on investigations into how and to what extent the shape of dendrites and the distribution of A-type potassium channels (KA) across the dendrites might regulate the backward and forward propagation of action potentials. They focused on the dendritic branches beyond the primary trunks attached to the cell body (soma). Current limits on experimental techniques makes it impossible to directly test a living cell so the research team used realistic computational models of a CA1 pyramidal cells using NEURON.

Note: The NEURON model associated with the paper being discussed is “CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)” and may be downloaded from the SenseLab ModelDB repository.

They used 27 three-dimensional (3D) digital reconstructions of rat CA1 pyramidal cells from four different laboratories, which are publicly available from NeuroMorpho.org. The reference (URL) to these data in the paper and in the ModelDB record is out of date. I wondered how I’d figure out which neurons were used in this study until I finally noticed that the cell identifier numbers used by NeuroMorpho.org were listed in figure 1 under the bars representing input resistance values. However, only 26 neurons were listed there. However, as I began looking at the records on NeuroMorpho.org I noticed the following. The listing 9068802 does not exist in the repository but 9068802a and 9068802b do exist (which would account for missing neuron number 27). This raises the question of the input resistance values for these two neurons. Neuron 8228804 is not in repository but 8228804a does exist. Kr1 and Kr2 do not exist but there is an NM1 and an NM2. I couldn’t find c8076 but there is a c8076e.

Note: Those interested may find the complete list of reconstructed neurons used in this study in the table at the end of this post.

This points out a huge issue that is super important for the Semantic Web. Things on the Semantic Web, like neurons, each have their unique identifier that is technically a Uniform Resource Identifier (URI) but often in practice is a Uniform Resource Locator (URL), which is a kind of URI. URIs and URLs used to identify things must remain valid across time to be useful. A published account of neuron n125 is compromised if its identifier http://neuromorpho.org/neuroMorpho/neuron_info.jsp?neuron_name=n125 (the way NeuroMorpho.org identifies their cells) is no longer valid. How do you find it? Ways to solve the problem have been proposed and even put into practice but the Semantic Web community hasn’t come to a consensus yet.

The study demonstrated that both back and forward propagation of action potentials in the dendrites can be effectively and independently modulated in individual dendritic branches of the same neuron. They found that the distribution of A-type potassium channels (KA) appears to be pivotal in gating back propagation whereas local morphology plays the lead role in regulating forward propagation. They found that spikes back propagating from the soma continuously decreased in size in the apical trunk but tended only to invade branches off the trunk in an all-or-none fashion. They also found that isolated spiking activity in a dendritic branch usually had negligible effects on the rest of the neuron.

You can play with these anatomical reconstructions and physiological simulations due to the remarkable tools and data freely available to anyone with access to the Internet. For instance, reproduce the paper’s figure 2a by running the “CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)” model downloaded from ModelDB in NEURON.

Note: Those with more than a passing interested in using NEURON may find the book or e-book by its creators useful “The NEURON Book.”

• FIrst, if you don’t already have it, download NEURON from the NEURON Download and install web page.

Note: NEURON runs under most computing environments. Details on setup and trouble shooting vary by platform but are well documented at the NEURON website.

• Next download the “CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)” model from ModelDB.

Note: Don’t forget to compile the files in the project folder. On the Macintosh computer you drag the project folder to NEURON’s mknrndll program icon.

• Load the model’s fig2A.hoc file.

The NEURON application should be running and displaying some windows. One window should contain an image of the hippocampus CA1 pyramidal neuron that you’ll use in the simulation (neuron 5038804). Another window titled “Neuron” shows two empty graphs and a button. The top graph will show a trace of the somatic membrane potential. The bottom graph will show the distribution of the average peak depolarization of each dendritic branch in the neuron.

• Click on the “runm” button to run the model.

The result should look like Figure 1 above and the paper’s figure 2a.

Don’t forget to play around with these models. You have first rate scientific tools at your disposal to investigate one of the biggest questions we may ask about ourselves. How does our brain work?

Note: Would you like me to take you deeper into NEURON? Please let me know!

The following table identifies all 27 neurons used in the study described in the paper under review. I’ve done the best I can to identify the cells (see text above) but there could be errors. Each neuron is identified by its NeuroMorpho.org number and is linked to its record in the repository.

5038801	9138801	c73162	cd2351
9068802a	NM1	cd0351	c80761
9068802b	NM2	c91665	c70963
6028801	n400	c62564	c30465
5038804	c9236e	c8076e	c72965
n125	c91662	c81462	c70863
8228804a	cd1152	c20466

Other related blog posts:

Brain Modeling Using NEURON, Interneurons, and Resonant Circuits

Brain Modeling Using NEURON: Superficial Pyramidal, Deep Pyramidal, Aspiny, and Stellate Neurons

Brain Modeling Using NEURON: Neural Activity Underlying Magnetoencephalography

NEURON, SenseLab ModelDB, NeuroMorpho.org, and Signal Processing in Brain Microcircuitry

A Taste of Neuroscience Papers in the Future

Kudos to the authors of the paper “Local Control of Postinhibitory Rebound Spiking in CA1 Pyramidal Neuron Dendrites” (published May 5, 2010 in the Journal of Neuroscience) described yesterday in my blog post “NEURON, SenseLab ModelDB, NeuroMorpho.org, and Signal Processing in Brain Microcircuitry.” Not only did they use the valuable resources provided by the NEURON simulator, SenseLab’s ModelDB repository, and the NeuroMorpho.org data repository but they also published their new Hippocampus Cornu Ammonis area 1 (CA1) pyramidal neuron model to ModelDB.

Note: Their hypothesis and conclusions were also very interesting and important but in this post I’m focusing on the future of neuroscience papers. For more about the reported findings please see yesterday’s blog post.

Note: A 3D graphic animation of neuron pc2b is displayed in Figure 1 of yesterday’s blog post. Neuron pc2b is from the NeuroMorpho.org data repository.

You can reproduce the paper’s figure 2b by running the “A1 pyramidal neuron: rebound spiking (Ascoli et al.2010)” model downloaded from ModelDB in NEURON.

• FIrst download NEURON from the NEURON Download and install web page.

Note: NEURON runs under most computing environments. Details on setup and trouble shooting vary by platform but are well documented at the NEURON website.

• Next download the “A1 pyramidal neuron: rebound spiking (Ascoli et al.2010)” model from ModelDB.

Note: Don’t forget to compile the files in the project folder. On the Macintosh computer you drag the project folder to NEURON’s mknrndll program icon.

• Load the model’s mosinit.hoc file.

The NEURON application should be running and displaying some windows. One window should contain five buttons labeled K_A, I_h, control, 4-AP, and 4-AP+ZD. The default K_A and I_h settings are the same as in the paper’s Figure 2d. Click on the control, 4-AP, or 4-AP+ZD button to run the model under each condition and get membrane potential traces from the soma (black) and dendrite (red). Figure 1 above shows the traces after clicking the “4-AP” button.

Note: Figure 2d in the paper places a trace from each of the three conditions (control, 4-AP, and 4-AP+ZD) together for the dendritic recording (top) and somatic recording (bottom). My Figure 1 above is of the somatic recording (black) and dendritic recording (red) under one condition (4-AP). However, all of the parameters and variables are the same.

This is a glimpse into how neuroscience will be published in the future. You won’t simply read a paper from a journal anymore. In fact, for the most part this already isn’t the case. It’s becoming rare to read an article that doesn’t include extensive online supplementary material. However, in the future research will be communicated in a seamless virtual environment that will enable you to play with models and drill into data while you read text.

Note: Those with more than a passing interested in using NEURON may find the book or e-book by its creators useful “The NEURON Book.”

Other related blog posts:

Brain Modeling Using NEURON: Superficial Pyramidal, Deep Pyramidal, Aspiny, and Stellate Neurons

Mandatory Publication of Computational Brain Models Simultaneously with Paper!

NEURON, SenseLab ModelDB, NeuroMorpho.org, and Signal Processing in Brain Microcircuitry