Donald Doherty's blog

Author: Donald Doherty

  • Fingers Elicit Directional Responses in Human Nerve Cells

    Since I recently posted on directional tuning in the rodent whisker system I want to point out that it’s been shown that when human finger tips touch objects they elicit directionally tuned responses in nerve cells carrying the signals to the brain.

    Nearly a decade ago Ingvars Birznieks and colleagues showed that nerve cells carrying signals from human fingertips responded with the maximum number of impulses to a force applied in a particular direction (“Encoding of Direction of Fingertip Forces by Human Tactile Afferents” published in the October 15, 2001 issue of the Journal for Neuroscience).

    A few years later Roland Johansson and Ingvars Birznieks showed that the relative timing of the first nerve impulses traveling to the brain carry reliable information about the direction of force and the shape of the surface contacting the fingertip (“First spikes in ensembles of human tactile afferents code complex spatial fingertip events” published in the February 2004 issue of Nature Neuroscience).

    Other related blog posts:

    Wiggling Whiskers: Directional Tuning

  • Wiggling Whiskers: Directional Tuning

    Brain cells that respond to whisker stimulation in rats have been shown to be tuned to the direction that the whisker is moved. The 30 or so large whiskers on either side of a rat’s snout work together to explore the environment.

    A study published in the January 20, 2010 issue of the Journal of Neuroscience titled “Feedforward Inhibition Determines the Angular Tuning of Vibrissal Responses in the Principal Trigeminal Nucleus” examines directional (angular) tuning responses of brain cells in the first processing station in the brain.

    Two important results of the study were:

    1) directional tuning is may be observed in these brain cells by counting the number of impulses evoked in each direction and observing the direction that evokes the most brain impulses or observing he direction that evokes impulses in the shortest time from when the whisker was moved

    2) inhibition in this first whisker related processing station in the brain sharpens directional tuning.

    The contribution of inhibition in sharpening directional selectivity of whisker (technically vibrissae) responses may prove important for retaining a relatively high degree of directional tuning in the brain. Modulation of inhibition could also enable the brain to dynamically influence the sharpness of directional tuning.

    Showing that directional tuning may be measured by impulse timing is a major contribution of this paper.

    Future posts to this blog will address the significance of these and other findings to the rat’s (and our own) ability to explore objects by touching them.

    Other related blog posts:

    Wiggling Whiskers for a Living?

  • Brain Research Using Online Data Repositories: Predicting Alzheimer’s Disease IV

    Predict Alzheimer’s disease with a “100 percent accurate” test? That’s what an New York Times article reported on August 9, 2010.

    We’ll now look into the basis of the claim as I promised in my “Brain Research Using Online Data Repositories: Predicting Alzheimer’s Disease II” blog post.

    What does the original research article actually say?

    The paper “Diagnosis-Independent Alzheimer Disease Biomarker Signature in Cognitively Normal Elderly People” by Geert De Meyer and colleagues was published in the August 2010 Archives of Neurology.

    There are two exciting methods behind this paper. First, is the use of an online research data repository that brings together a mass of Alzheimer’s disease related clinical data from a number of labs known as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data repository.

    The second is the use of what the authors’ call a “mixture modeling approach.” The approach boils down to associating each kind of protein measured in the cerebrospinal fluid of patients with a dimension in a clustering algorithm.

    They clustered Amyloid beta (2 components of 1 biomarker) or Amyloid beta and tau (2 biomarkers) using an unsupervised learning method. Only after this was done were the clinical diagnoses looked at to see if data clusters were associated with normal, mild cognitive impairment, or Alzheimer’s disease. In all cases, two clusters emerged. One associated with healthy cognitive function and the other with Alzheimer’s disease.

    Using the mixed modeling approach, the cerebrospinal fluid level of the two biomarkers in 57 patients with Mild Cognitive Impairment (MCI) predicted with 100% accuracy the individuals that would progress to Alzheimer’s disease (clinically measured) over the next 5 years. This is the data cited by the New York Times.

    All of the patients in the population showing 100% predictability already had noticeable problems with memory and other cognitive abilities when the measurements were taken. The same mixed modeling approach showed a 94% accuracy when applied to a group of autopsy confirmed Alzheimer’s disease patients.

    Also in the current paper the authors showed a 93% accuracy at predicting Alzheimer’s disease using just Amyloid beta as the biomarker (autopsy confirmed Alzheimer’s disease) which is essentially the same as the number obtained when using two biomarkers (94% above). Finally, previous papers have shown comparable numbers using other methods (Oskar Hansson et al., 2006; Sebastiaan Engelborghs et al., 2008).

    In sum, it seems clear that this paper did not set out to demonstrate a more accurate method for the early prediction of Alzheimer’s disease. What the paper does seem to demonstrate is an unbiased way to parse data into clusters that may be shown to correlate with disease related outcomes.

    Other related blog posts:

    Brain Research Using Online Data Repositories: Predicting Alzheimer’s Disease

    Brain Research Using Online Data Repositories: Predicting Alzheimer’s Disease II

    Brain Research Using Online Data Repositories: Predicting Alzheimer’s Disease III