TY - JOUR
T1 - Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning
AU - Janssen, Ronald J
AU - Mourão-Miranda, Janaina
AU - Schnack, Hugo G
N1 - Funding Information:
This work was supported Wellcome Trust Grant No. WT102845/Z/13/Z (to JM-M).
Funding Information:
This work was supported Wellcome Trust Grant No. WT102845/Z/13/Z (to JM-M).
Publisher Copyright:
© 2018 Society of Biological Psychiatry
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine-learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine-learning–based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers.
AB - Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine-learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine-learning–based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers.
KW - Humans
KW - Machine Learning
KW - Mental Disorders/diagnosis
KW - Neuroimaging/methods
KW - Prognosis
KW - Psychiatry/methods
UR - http://www.scopus.com/inward/record.url?scp=85047097372&partnerID=8YFLogxK
U2 - 10.1016/j.bpsc.2018.04.004
DO - 10.1016/j.bpsc.2018.04.004
M3 - Review article
C2 - 29789268
AN - SCOPUS:85047097372
SN - 2451-9022
VL - 3
SP - 798
EP - 808
JO - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
JF - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
IS - 9
ER -