Literature DB >> 26898163

Painful Issues in Pain Prediction.

Li Hu1, Gian Domenico Iannetti2.   

Abstract

How perception of pain emerges from neural activity is largely unknown. Identifying a neural 'pain signature' and deriving a way to predict perceived pain from brain activity would have enormous basic and clinical implications. Researchers are increasingly turning to functional brain imaging, often applying machine-learning algorithms to infer that pain perception occurred. Yet, such sophisticated analyses are fraught with interpretive difficulties. Here, we highlight some common and troublesome problems in the literature, and suggest methods to ensure researchers draw accurate conclusions from their results. Since functional brain imaging is increasingly finding practical applications with real-world consequences, it is critical to interpret brain scans accurately, because decisions based on neural data will only be as good as the science behind them.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  functional magnetic resonance imaging (fMRI); machine learning; multivariate pattern analysis (MVPA); pain; pain signature; prediction

Mesh:

Year:  2016        PMID: 26898163     DOI: 10.1016/j.tins.2016.01.004

Source DB:  PubMed          Journal:  Trends Neurosci        ISSN: 0166-2236            Impact factor:   13.837


  26 in total

Review 1.  Distinguishing pain from nociception, salience, and arousal: How autonomic nervous system activity can improve neuroimaging tests of specificity.

Authors:  In-Seon Lee; Elizabeth A Necka; Lauren Y Atlas
Journal:  Neuroimage       Date:  2019-10-08       Impact factor: 6.556

2.  Altered white matter microarchitecture in the cingulum bundle in women with primary dysmenorrhea: A tract-based analysis study.

Authors:  Jixin Liu; Hongjuan Liu; Junya Mu; Qing Xu; Tao Chen; Wanghuan Dun; Jing Yang; Jie Tian; Li Hu; Ming Zhang
Journal:  Hum Brain Mapp       Date:  2017-06-07       Impact factor: 5.038

Review 3.  Primer on machine learning: utilization of large data set analyses to individualize pain management.

Authors:  Parisa Rashidi; David A Edwards; Patrick J Tighe
Journal:  Curr Opin Anaesthesiol       Date:  2019-10       Impact factor: 2.706

4.  Limits of decoding mental states with fMRI.

Authors:  Rami Jabakhanji; Andrew D Vigotsky; Jannis Bielefeld; Lejian Huang; Marwan N Baliki; Giandomenico Iannetti; A Vania Apkarian
Journal:  Cortex       Date:  2022-01-31       Impact factor: 4.644

5.  Classification of primary dysmenorrhea by brain effective connectivity of the amygdala: a machine learning study.

Authors:  Siyi Yu; Liying Liu; Ling Chen; Menghua Su; Zhifu Shen; Lu Yang; Aijia Li; Wei Wei; Xiaoli Guo; Xiaojuan Hong; Jie Yang
Journal:  Brain Imaging Behav       Date:  2022-10-18       Impact factor: 3.224

6.  Pharmacologic attenuation of cross-modal sensory augmentation within the chronic pain insula.

Authors:  Steven E Harte; Eric Ichesco; Johnson P Hampson; Scott J Peltier; Tobias Schmidt-Wilcke; Daniel J Clauw; Richard E Harris
Journal:  Pain       Date:  2016-09       Impact factor: 7.926

7.  Editorial: Modeling of Visual Cognition, Body Sense, Motor Control and Their Integrations.

Authors:  Hong Qiao; Li Hu
Journal:  Front Comput Neurosci       Date:  2016-12-27       Impact factor: 2.380

Review 8.  Magnetic resonance imaging for chronic pain: diagnosis, manipulation, and biomarkers.

Authors:  Yiheng Tu; Jin Cao; Yanzhi Bi; Li Hu
Journal:  Sci China Life Sci       Date:  2020-11-23       Impact factor: 6.038

9.  Advances in multivariate pattern analysis for chronic pain: an emerging, but imperfect method.

Authors:  Massieh Moayedi
Journal:  Pain Rep       Date:  2016-12-11

Review 10.  Legal and ethical issues of using brain imaging to diagnose pain.

Authors:  Karen D Davis
Journal:  Pain Rep       Date:  2016-11-30
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