Introduction Advanced machine learning methods might help to identify dementia risk from
neuroimaging, but their accuracy to date is unclear. Methods We systematically reviewed the …

… variety of cognitive tasks and processes. In parallel, the advent of machine learning has …
has been done combining machine learning with functional neuroimaging to advance the …

… machine learning … on machine learning as applied to functional connectivity with magnetic
resonance imaging, as an example to ground discussion. We argue that for machine learning …

… Neuroimaging applications using machine learning in regard to … on machine learning
applications in neuroimaging. Rajus et al. (2019) has proposed the application of machine learning …

… Machine learning techniques are especially suited to tackle this challenging problem. This …
introduction to machine learning in the context of its application to clinical neuroimaging data. …

In this paper, we provide an extensive overview of machine learning techniques applied to
structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically …

… Even when using a smaller number of regions of interest (ROIs) instead of voxels, combining
multiple data sources (such as neuroimaging data and cognitive data or demographics), …

… Machine learning was used to construct MCI prediction models, comparing the area under
the … Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and …

Dementia, a chronic and progressive cognitive declination of … In recent years, neuroimaging
with computer-aided … to the application of machine learning techniques for neuroimaging. In …

… This systematic review synthesizes the most recent neuroimaging procedures and machine
learning approaches for the prediction of conversion from mild cognitive impairment to …