Abstract
There has been a recent upsurge of reports about applications of pattern-recognition techniques from the field of machine learning to functional MR imaging data as a diagnostic tool for systemic brain disease or psychiatric disorders. Entities studied include depression, schizophrenia, attention deficit hyperactivity disorder, and neurodegenerative disorders like Alzheimer dementia. We review these recent studies which-despite the optimism from some articles-predominantly constitute explorative efforts at the proof-of-concept level. There is some evidence that, in particular, support vector machines seem to be promising. However, the field is still far from real clinical application, and much work has to be done regarding data preprocessing, model optimization, and validation. Reporting standards are proposed to facilitate future meta-analyses or systematic reviews.
Highlights
Each time a different range of datasets, often exactly one in the there are a large number of supervised machine- case of leave-one-out CV, is excluded and used as a test set.[19] learning techniques that can, in principle, be applied in this context,[23 2] groups of methodologies are of particular importance: Recent Diagnostic fMRI Approaches Based on multivoxel pattern analyses (MVPAs)
A significant number of studies[55,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97] have been based on task-free fMRI acquisitions, so-called resting-state fMRI, which focuses on the functional connectivity of distant brain regions in terms of signal cofluctuations and on the integrity of large-scale brain networks.[98,99]
There is usually no formal test that allows conclusions regarding the ability of whole MVPA approaches to construct successful diagnostic tools in a particular clinical setting because CV is only used to assess classification of new data but not reliable classifier training independent from particular subjects
Summary
Each time a different range of datasets, often exactly one in the there are a large number of supervised machine- case of leave-one-out CV, is excluded and used as a test set.[19] learning techniques that can, in principle, be applied in this context,[23 2] groups of methodologies are of particular importance: Recent Diagnostic fMRI Approaches Based on MVPA
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