Rhesus Monkey Layer 3 Pyramidal Neurons: Young vs aged PFC (Coskren et al. 2015)

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Layer 3 (L3) pyramidal neurons in the lateral prefrontal cortex (LPFC) of rhesus monkeys exhibit dendritic regression, spine loss and increased action potential (AP) firing rates during normal aging. The relationship between these structural and functional alterations, if any, is unknown. Computational models using the digital reconstructions with Hodgkin-Huxley and AMPA channels allowed us to assess relationships between demonstrated age-related changes and to predict physiological changes that have not yet been tested empirically. Tuning passive parameters for each model predicted significantly higher membrane resistance (Rm) in aged versus young neurons. This Rm increase alone did not account for the empirically observed fI-curves, but coupling these Rm values with subtle differences in morphology and membrane capacitance Cm did. The predicted differences in passive parameters (or other parameters with similar effects) are mathematically plausible, but must be tested empirically.
1 . Coskren PJ, Luebke JI, Kabaso D, Wearne SL, Yadav A, Rumbell T, Hof PR, Weaver CM (2015) Functional consequences of age-related morphologic changes to pyramidal neurons of the rhesus monkey prefrontal cortex. J Comput Neurosci 38:263-83 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L2/3 pyramidal GLU cell;
Channel(s): I Na,t; I A; I K; I M; I h; I K,Ca; I Calcium; I_AHP;
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Influence of Dendritic Geometry; Detailed Neuronal Models; Action Potentials; Aging/Alzheimer`s;
Implementer(s): Weaver, Christina [christina.weaver at fandm.edu];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; I Na,t; I A; I K; I M; I h; I K,Ca; I Calcium; I_AHP;
/* Sets nseg in each section to an odd value
   so that its segments are no longer than
     d_lambda x the AC length constant
   at frequency freq in that section.

   Be sure to specify your own Ra and cm before calling geom_nseg()

   To understand why this works,
   and the advantages of using an odd value for nseg,
   see  Hines, M.L. and Carnevale, N.T.
        NEURON: a tool for neuroscientists.
        The Neuroscientist 7:123-135, 2001.

stifleCharCount = printf("Loading fixnseg.hoc\n")

// these are reasonable values for most models
// freq = 100      // Hz, frequency at which AC length constant will be computed
// d_lambda = 0.1

func lambda_f() { local i, x1, x2, d1, d2, lam
  if (n3d() < 2) {
          return 1e5 * sqrt(diam / (4 * PI * $1 * Ra * cm))
  // above was too inaccurate with large variation in 3d diameter
  // so now we use all 3-d points to get a better approximate lambda
  x1 = arc3d(0)
  d1 = diam3d(0)
  lam = 0
  for i = 1, n3d() - 1 {
    x2 = arc3d(i)
    d2 = diam3d(i)
    lam += (x2 - x1)/sqrt(d1 + d2)
    x1 = x2
    d1 = d2
  //  length of the section in units of lambda
  lam *= sqrt(2) * 1e-5 * sqrt(4 * PI * $1 * Ra * cm)

  return L / lam

proc geom_nseg() { local freq, d_lambda
  freq = $1
  d_lambda = $2
  forall {
    nseg = int((L / (d_lambda * lambda_f(freq)) + 0.9) / 2) * 2 + 1

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