  The human brain is a complex network comprised of functionally and
anatomically interconnected brain regions. A growing number of studies have
suggested that empirical estimates of brain networks may be useful for
discovery of biomarkers of disease and cognitive state. A prerequisite for
realizing this aim, however, is that brain networks also serve as reliable
markers of an individual. Here, using Human Connectome Project data, we build
upon recent studies examining brain-based fingerprints of individual subjects
and cognitive states based on cognitively-demanding tasks that assess, for
example, working memory, theory of mind, and motor function. Our approach
achieves accuracy of up to 99\% for both identification of the subject of an
fMRI scan, and for classification of the cognitive state of a previously-unseen
subject in a scan. More broadly, we explore the accuracy and reliability of
five different machine learning techniques on subject fingerprinting and
cognitive state decoding objectives, using functional connectivity data from
fMRI scans of a high number of subjects (865) across a number of cognitive
states (8). These results represent an advance on existing techniques for
functional connectivity-based brain fingerprinting and state decoding.
Additionally, 16 different pre-processing pipelines are compared in order to
characterize the effects of different aspects of the production of functional
connectomes (FCs) on the accuracy of subject and task classification, and to
identify possible confounds.
