  In spite of years of research, the mechanisms that underlie the development
of schizophrenia, as well as its relapse, symptomatology, and treatment,
continue to be a mystery. The absence of appropriate analytic tools to deal
with the variable and complicated nature of schizophrenia may be one of the
factors that contribute to the development of this disorder. Deep learning is a
subfield of artificial intelligence that was inspired by the nervous system. In
recent years, deep learning has made it easier to model and analyse
complicated, high-dimensional, and nonlinear systems. Research on schizophrenia
is one of the many areas of study that has been revolutionised as a result of
the outstanding accuracy that deep learning algorithms have demonstrated in
classification and prediction tasks. Deep learning has the potential to become
a powerful tool for understanding the mechanisms that are at the root of
schizophrenia. In addition, a growing variety of techniques aimed at improving
model interpretability and causal reasoning are contributing to this trend.
Using multi-site fMRI data and a variety of deep learning approaches, this
study seeks to identify different types of schizophrenia. Our proposed method
of temporal aggregation of the 4D fMRI data outperforms existing work. In
addition, this study aims to shed light on the strength of connections between
various brain areas in schizophrenia individuals.
