Tag Archives: Open Data Repositories

Making Neuroinformatics Integral to Studying the Brain

The current issue of Science includes the special section Dealing with Data. In it there is a perspective paper on neuroscience and data titled “Challenges and Opportunities in Mining Neuroscience Data” (published February 11, 2011 in Science). The focus of the perspective is on the Human Connectome Project and the Neuroscience Information Framework. These important projects are familiar to those of you who have been following my posts (see “Other related blog posts” below). However, the authors point out a very important fact. Most neuroinformatics resources remain underused by the research community.

The February 14, 2011 cover of Science. The issue includes a special section Dealing with Data.
Figure 1. The February 14, 2011 cover of Science. The issue includes a special section Dealing with Data.

The authors conclude with eight suggestions on how to make neuroinformatics integral to studying the brain:

  • Neuroscientists should share their data and in a form that is easily accessible.
  • Neuroscience databases need to be created, populated, and sustained with adequate support from federal and other funding mechanisms.
  • Databases become more useful as they are more densely populated so adding to existing databases may be preferable to creating new ones.
  • Data consumption will increasingly involve machines first and humans second. Neuroscientists should annotate content using community ontologies and identifiers. Coordinates, atlas, and registration method should be specified when referencing spatial locations.
  • Some types of published data should be reported in standardized table formats that facilitate data mining.
  • Investment needs to occur in interdisciplinary research to develop computational, machine-learning, and visualization methods for synthesizing across spatial and temporal information tiers.
  • Educational strategies from undergraduate through postdoctoral levels are needed to ensure that neuroscientists of the next generation are proficient in data mining and using the data-sharing tools of the future.
  • Cultural changes in the neurosciences are needed to promote widespread participation in this endeavor.

These suggestions, if followed, would certainly move the neuroscience community in the right direction. They seem to assume, however, that human consumption of neuroscience data will remain primarily as it is. I don’t think this is the case. We will see radical changes in human data consumption as machines become able to do more with the data without human intervention. The suggestion to use “standardized table formats” in relational databases is good but what I think would even be better is to focus efforts on getting the data deployed to the Semantic Web. Nevertheless, these two goals are not mutually exclusive.


Other related blog posts:

Mapping the Brain’s Connections: the Connectome

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

NIF: Neurons, Models, and Grants

NIF: Better Literature Search

Neurotrophic Factors and Parkinson’s Disease: Need for a Common Database and Systems Biology Models

The death of dopaminergic neurons in the substantia nigra is a major pathology underlying Parkinson’s disease. Therefore it makes a lot of sense to look to neurotrophic factors, the most potent mediators of neuronal survival identified to date, as promising therapeutic agents for saving these neurons. A new review paper “Repairing the parkinsonian brain with neurotrophic factors” (published February 2011 in Trends in Neurosciences) states that, so far, clinical trials of neurotrophic factors to treat Parkinson’s disease have been disappointing. Why?

An overview of glial cell line-derived neurotrophic factor (GDNF) signaling.
Figure 1. An overview of glial cell line-derived neurotrophic factor (GDNF) signaling. Figure 1 from “Repairing the parkinsonian brain with neurotrophic factors” by Liviu Aron and Rudiger Klein. Trends in Neurosciences, February 2011.

Details of molecular signal pathways are known for some neurotrophic factors. For example, one can follow the glial cell line-derived neurotrophic factor (GDNF) signal step-by-step (see Figure 1 above; for text describing the figure please see the review paper). Nevertheless, even for GDNF, the outcomes of the interactions are poorly understood and it remains to be determined what interactions actually promote survival of dopaminergic neurons. The authors of the review conclude that we simply do not know enough yet about how neurotrophic factors work.

The authors state that “the experimental evidence that neurotrophic factor disturbances alone cause Parkinson’s disease is currently weak.” Nevertheless, they cite the following to encourage continued research in this area:

  • Parkinson’s disease-associated genes require an intact neurotrophic factor network to promote substantia nigra neuron survival during aging.
  • Changes induced by mutations in Parkinson’s disease-associated genes decrease the efficacy of neurotrophic factor signaling.
  • Shared substrates between neurotrophic factors and Parkinson’s disease-associated proteins might represent new targets for drug development in Parkinson’s disease.

Certainly research in neurotrophic factors and Parkinson’s disease must continue to move forward. Interestingly there were two sentences in the review’s conclusion that seemed tacked on and were not discussed:

  • The creation of a common database with results from standardized experiments could result in a systems biology approach in experimental Parkinson’s disease.
  • Mathematical models of neurotrophic factor action could then be used to predict and test new cellular targets.

Creating a common database and associated systems biology models may accelerate the field by enabling a broad set of scientists to understand the complex signaling pathways underlying neurotrophic factor function and the way they may be applied to helping Parkinson’s disease patients.

NIH and Other Major Funding Biomedical Research Institutions Call for Sharing Research Data

“As funders of public health research, we need to ensure that research outputs are used to maximize knowledge and potential health benefits. In turn, the populations who participate in research, and the taxpayers who foot the bill, have the right to expect that every last ounce of knowledge will be wrung from the research.”

The above statement concludes the first paragraph of a comment “Sharing research data to improve public health” published January 7, 2011 in the Lancet and signed by 17 major biomedical research funding institutions from across the globe. The published comment goes on to state that data are often treated as private property by investigators “who aim to maximize their publication record at the expense of the widest possible use of the data.”

Note: Excerpts of a joint statement of purpose are published in the comment. The full statement is available online here.

Since taxpayers foot most of the bill for research, the data are really owned by the public. Nevertheless, the competition is intense for the biomedical researcher working to build his or her career. The main determining factor for getting ahead is the number of papers that the individual has published. Funding agencies have an obligation to maximize data use – an obligation to both the people footing the bill and also to the subjects that participate in the research – but they also have an obligation to the research scientists who put so much of their life into time consuming, difficult, and extremely important research.

Meeting all of these apparently contradictory obligations may be less than the impossible task that it seems at first glance. Transition the biomedical research culture – the funders, the university administrations that promote their scientists, etcetera – to appreciate and fund the scientists’ producing solid data and producing the largest impact on their field. Not by counting their direct publications. This is the easiest thing to do but has lead to a massive amount of literature that is redundant or worse. With current technology we can easily quantify the amount of data scientist contributes to online data repositories. We can also easily track the use of those data. Perhaps these measures could help to replace the old measure of the number of papers published.