Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Abstract

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there are extensive evidences showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

Single Subject Classification of Alzheimer’s Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging

“…However, despite these detectable group differences, MRI-based single subject classifications remain challenging. Per subject analyses are affected more by between subject variations than group-based analyses and it is still unclear which MRI measure contributes most to MRI-based dementiatype classifications [18][19][20]. Particularly in the earlier disease stages, accurate dementia-type classifications based on structural neuroimaging alone may be hampered by atrophy and tract specific deficit patterns that overlap or are hardly distinguishable [17,21,22].…”

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

“…6 less than). FNC has also been widely used to identify group differences or even individual subject classification, 30–32 and differences in FNC can profitably be analyzed for associations with symptoms or quantitative characteristics.…”

“…19,20,35 These approaches are very useful when trying to use ICA for single-subject prediction, because they ensure that the data on which the analysis is predicted are completely separate from the data that were used to generate the maps and the prediction algorithm. 32 …”

Lost in translation

“…There is substantial evidence that the neural representations underlying multiple forms of cognition, emotion, and action are population codes distributed across large numbers of neurons and (in the case of fMRI) brain regions and systems (e.g., for review, see Kragel et al 2018 1 ). In fMRI studies, distributed predictive models that include activity across regions and systems can dramatically outperform those based on even the best single brain regions (for recent reviews see, e.g., Arbabshirani et al 2017 2 ; Bzdok & Meyer-Lindenberg 2017 3 ; Woo et al 2017 4 ; Kragel et al 2018, Figure 3 and text). …”