Polymer Physics-Based Classification of Neurons

AbstractRecognizing that diverse morphologies of neurons are reminiscent of structures of branched polymers, we put forward a principled and systematic way of classifying neurons that employs the ideas of polymer physics. In particular, we use 3D coordinates of individual neurons, which are accessible in recent neuron reconstruction datasets from electron microscope images. We numerically calculate the form factor,F(q), a Fourier transform of the distance distribution of particles comprising an object of interest, which is routinely measured in scattering experiments to quantitatively characterize the structure of materials. For a polymer-like object consisting ofn monomers spanning over a length scale ofr,F(q) scales with the wavenumber\(q(=2\pi /r)\) as\(F(q)\sim q^{-\mathcal {D}}\) at an intermediate range ofq, where\(\mathcal {D}\) is the fractal dimension or the inverse scaling exponent (\(\mathcal {D}=\nu ^{-1}\)) characterizing the geometrical feature (\(r\sim n^{\nu }\)) of the object.F(q) can be used to describe a neuron morphology in terms of its size (\(R_n\)) and the extent of branching quantified by\(\mathcal {D}\). By defining the distance betweenF(q)s as a measure of similarity between two neuronal morphologies, we tackle the neuron classification problem. In comparison with other existing classification methods for neuronal morphologies, ourF(q)-based classification rests solely on 3D coordinates of neurons with no prior knowledge of morphological features. Wh...
Source: Neuroinformatics - Category: Neuroscience Source Type: research