IT'S A MESSY NETWORK!

Brain connectome is the map to explore the neuron world. Neural signals synchronization and coordination between distant brain regions within a connectome provides key to execution and memory ordered sequence of events.

Connectomics is complicated and it involves the measure of soma, dendrites, axonal path, and branching patterns together with the synapses and gap junctions of the neurons involved in any given brain circuit or network.

Brain constitutes of anatomical and functional networks. Structural networks making connections make up the “connectome”. These networks which are formed by axons which extend through distant regions to make synapses making it challenging to map the connectomes and requires multiscale approaches. Through years multiple tools have been developed to map the brain from the tracing of mesoscopic axonal connections and the delineation of white matter tracts, the mapping of neurons organized into functional circuits to the identification of cellular-level connections, and the molecular properties of individual synapses.

Tracing of axonal and dendritic arborizations require light microscopy whereas for identifying synapses and subcellular components of pre- and post- synaptic components require electronic microscope. Despite all these both the LM & EM do not help in acquiring complete neuronal wiring diagram. The data obtained from anatomic measures still don’t provide complete information (such as insufficient resolution) sufficient enough to build a connectome. To fill the gap this can be achieved through Bayesian modeling and data fusion which is obtained from DTI. This Bayesian analysis is advantageous as it provides values to unknown parameters, which in turn becomes the probability distribution for those unknown parameters.

Neuroimaging techniques to infer structural and functional connectome at a macroscopic scale have emerged, among which diffusion MRI tractography is the principal tool for building structural connectome. Functional networks are derived from time series data from neural recordings. The neural networks are different for every individual and so it is important to know more about the variability and keep some of the brain function as constant. Will it be possible to identify treatment methods when there’s variability between every individual’s connectome? There are rearrangements often seen in molecular and cellular components. So, it is important to include structural plasticity in the model.