Recent research suggests that any set of first order linear vector ODEs can be converted to a set of specific vector ODEs adhering to what we have called "Quantum Harmonical Form (QHF)". QHF has been developed using a virtual quantum multi harmonic oscillator system where mass and force constants are considered to be time variant and the Hamiltonian is defined as a conic structure over positions and momenta to conserve the Hermiticity. As described in previous works, the conversion to QHF requires the matrix coefficient of the first set of ODEs to be a normal matrix. In this paper, this limitation is circumvented using a space extension approach expanding the potential applicability of this method. Overall, conversion to QHF allows the investigation of a set of ODEs using mathematical tools available to the investigation of the physical concepts underlying quantum harmonic oscillators. The utility of QHF in the context of dynamical systems and dynamical causal modeling in behavioral and cognitive neuroscience is briefly discussed.There is a large number of mathematical tools available for the investigation of the dynamics within quantum mechanical systems. Recently our research group has been successful in relating ODEs to dynamics of quantum mechanical systems [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] through the equations of motion for the expectations (expected values) of certain operators corresponding to observable entities such as position and momentum of quantum harmonic oscillators. Recent work has shown that, a subset of all linear vector ODEs with varying matrix coefficients and inhomogeneities can be converted to a set of two linear vector ODEs with certain matrix coefficients using a multi harmonic oscillator system with a time dependent Hamiltonian which has a conic structure in positions and momenta [1]. This framework requires the matrix coefficients of the original ODE to be normal, in other words, diagonalizable which is a fairly severe restriction excluding all types of matrices which cannot be diagonalized but can be reduced to Jordan canonical form from our analysis.ODEs are considerably important for modeling various dynamical systems in engineering as well as physical, natural and social sciences. Specifically, Dynamical Causal Modelling (DCM) [21,22] in neuroscience is an excellent example. The governing equations are typically first order linear vector ODEs and are often used to investigate the effective connectivity between various brain areas in functional imaging. On the other hand, direct applicability or utility of quantum mechanics and quantum mechanical tools within neurophysiological research has been questioned [23]. Approaches entailing quantum treatment of exocytosis, links the neocortical activity with the existence of a large number of quantum probability amplitudes, where mental intention is thought to manifest itself neurally via an increase in the probability for exocytoses in a complete dendron [24]. Phenomenological discussion of quantum states within the brain a...