  The human brain is one of the most complex living structures in the known
Universe. It consists of billions of neurons and synapses. Due to its intrinsic
complexity, it can be a formidable task to accurately depict brain's structure
and functionality. In the past, numerous studies have been conducted on
modeling brain disease, structure, and functionality. Some of these studies
have employed Agent-based approaches including multiagent-based simulation
models as well as brain complex networks. While these models have all been
developed using agent-based computing, however, to our best knowledge, none of
them have employed the use of Agent-Oriented Software Engineering (AOSE)
methodologies in developing the brain or disease model. This is a problem
because without due process, developed models can miss out on important
requirements. AOSE has the unique capability of merging concepts from
multiagent systems, agent-based modeling, artificial intelligence, besides
concepts from distributed systems. AOSE involves the various tested software
engineering principles in various phases of the model development ranging from
analysis, design, implementation, and testing phases. In this paper, we employ
the use of three different AOSE methodologies for modeling the Multiple
Sclerosis brain disease namely GAIA, TROPOS, and MASE. After developing the
models, we further employ the use of Exploratory Agent-based Modeling (EABM) to
develop an actual model replicating previous results as a proof of concept. The
key objective of this study is to demonstrate and explore the viability and
effectiveness of AOSE methodologies in the development of complex brain
structure and cognitive process models. Our key finding include demonstration
that AOSE methodologies can be considerably helpful in modeling various living
complex systems, in general, and the human brain, in particular.
