  Psychiatric disorders have historically been classified using symptom
information alone. With the advent of new technologies that allowed researchers
to investigate brain mechanisms more directly, interest in the mechanistic
rationale behind defined pathologies and aetiology redefinition has greatly
increased. This is particularly appealing for the field of personalised
medicine, which searches for data-driven approaches to improve individual
diagnosis, prognosis and treatment selection. Here we intend to systematically
analyse the usage of functional MRI on both the elucidation of psychiatric
disease biotypes and the interpretation of subtypes obtained via unsupervised
learning applied to symptom or biomarker data. We searched the existing
literature for functional MRI applications to the obtention or interpretation
of psychiatric disease subtypes. The PRISMA guidelines were applied to filter
the retrieved studies, and the active learning framework ASReviews was applied
for article prioritization. From the 20 studies that met the inclusion
criteria, 5 used functional MRI data to interpret symptom-derived disease
clusters, 4 used it for the interpretation of clusters derived from biomarker
data other than fMRI itself, and 11 applied clustering to fMRI directly. Major
depression disorder and schizophrenia were the two most studied pathologies,
followed by ADHD, psychosis, autism disorder, and early violence. No
trans-diagnostic studies were retrieved. While interest in personalised
medicine and data-driven disease subtyping is on the rise and psychiatry is not
the exception, unsupervised analyses of functional MRI data are inconsistent to
date, and much remains to be done in terms of gathering and centralising data,
standardising pipelines and model validation, and method refinement. The usage
of fMRI in the field of trans-diagnostic psychiatry remains vastly unexplored.
