| Autor: |
Qianqian Lin, Gan Huang, Linling Li, Li Zhang, Zhen Liang, Ahmed M. Anter, Zhiguo Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
NeuroImage, Vol 225, Iss , Pp 117506- (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1095-9572 |
| DOI: |
10.1016/j.neuroimage.2020.117506 |
| Popis: |
Using machine learning to predict the intensity of pain from fMRI has attracted rapidly increasing interests. However, due to remarkable inter- and intra-individual variabilities in pain responses, the performance of existing fMRI-based pain prediction models is far from satisfactory. The present study proposed a new approach which can design a prediction model specific to each individual or each experimental trial so that the specific model can achieve more accurate prediction of the intensity of nociceptive pain from single-trial fMRI responses. More precisely, the new approach uses a supervised k-means method on nociceptive-evoked fMRI responses to cluster individuals or trials into a set of subgroups, each of which has similar and consistent fMRI activation patterns. Then, for a new test individual/trial, the proposed approach chooses one subgroup of individuals/trials, which has the closest fMRI patterns to the test individual/trial, as training samples to train an individual-specific or a trial-specific pain prediction model. The new approach was tested on a nociceptive-evoked fMRI dataset and achieved significantly higher prediction accuracy than conventional non-specific models, which used all available training samples to train a model. The generalizability of the proposed approach is further validated by training specific models on one dataset and testing these models on an independent new dataset. This proposed individual-specific and trial-specific pain prediction approach has the potential to be used for the development of individualized and precise pain assessment tools in clinical practice. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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