Identifying neuropathic pain using 18F-FDG micro-PET: A multivariate pattern analysis. Kim, C. E., Kim, Y. K., Chung, G., Im, H. J., Lee, D. S., Kim, J., & Kim, S. J. NeuroImage, 86:311–316, 2014. Publisher: Elsevier Inc. ISBN: 1053-8119![Identifying neuropathic pain using 18F-FDG micro-PET: A multivariate pattern analysis [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Pain is a multidimensional experience emerging from the flow of information between multiple brain regions. A growing body of evidence suggests that pathological pain causes plastic changes of various brain regions. Here, we hypothesized that the induction of neuropathic pain alters distributed patterns of the resting-state brain activity in animal models, and capturing the altered pattern would enable identification of neuropathic pain at the individual level. We acquired micro-positron emission tomography with [18F]fluorodeoxyglucose (FDG micro-PET) images in awake rats with spinal nerve ligation (SNL) and without (sham) (SNL group, n=13; sham group, n=10). Multivariate pattern analysis (MVPA) with linear support vector machine (SVM) successfully identified the brain with SNL (92.31% sensitivity, 90.00% specificity, and 91.30% total accuracy). Predictive brain regions with increased metabolism were mainly located in prefrontal-limbic-brainstem areas including the anterior olfactory nucleus (AON), insular cortex (IC), piriform cortex (PC), septal area (SA), basal forebrain/preoptic area (BF/POA), amygdala (AMY), hypothalamus (HT), rostral ventromedial medulla (RVM) and the ventral midbrain (VMB). In contrast, predictive regions with decreased metabolism were observed in widespread cortical areas including secondary somatosensory cortex (S2), occipital cortex (OC), temporal cortex (TC), retrosplenial cortex (RSC), and the cerebellum (CBL). We also applied the univariate approach and obtained reduced prediction performance compared to MVPA. Our results suggest that developing neuroimaging-based diagnostic tools for pathological pain can be achieved by considering patterns of the resting-state brain activity. © 2013 Elsevier Inc.
@article{kim_identifying_2014,
title = {Identifying neuropathic pain using {18F}-{FDG} micro-{PET}: {A} multivariate pattern analysis},
volume = {86},
issn = {10959572},
url = {http://dx.doi.org/10.1016/j.neuroimage.2013.10.001},
doi = {10.1016/j.neuroimage.2013.10.001},
abstract = {Pain is a multidimensional experience emerging from the flow of information between multiple brain regions. A growing body of evidence suggests that pathological pain causes plastic changes of various brain regions. Here, we hypothesized that the induction of neuropathic pain alters distributed patterns of the resting-state brain activity in animal models, and capturing the altered pattern would enable identification of neuropathic pain at the individual level. We acquired micro-positron emission tomography with [18F]fluorodeoxyglucose (FDG micro-PET) images in awake rats with spinal nerve ligation (SNL) and without (sham) (SNL group, n=13; sham group, n=10). Multivariate pattern analysis (MVPA) with linear support vector machine (SVM) successfully identified the brain with SNL (92.31\% sensitivity, 90.00\% specificity, and 91.30\% total accuracy). Predictive brain regions with increased metabolism were mainly located in prefrontal-limbic-brainstem areas including the anterior olfactory nucleus (AON), insular cortex (IC), piriform cortex (PC), septal area (SA), basal forebrain/preoptic area (BF/POA), amygdala (AMY), hypothalamus (HT), rostral ventromedial medulla (RVM) and the ventral midbrain (VMB). In contrast, predictive regions with decreased metabolism were observed in widespread cortical areas including secondary somatosensory cortex (S2), occipital cortex (OC), temporal cortex (TC), retrosplenial cortex (RSC), and the cerebellum (CBL). We also applied the univariate approach and obtained reduced prediction performance compared to MVPA. Our results suggest that developing neuroimaging-based diagnostic tools for pathological pain can be achieved by considering patterns of the resting-state brain activity. © 2013 Elsevier Inc.},
journal = {NeuroImage},
author = {Kim, Chang Eop and Kim, Yu Kyeong and Chung, Geehoon and Im, Hyung Jun and Lee, Dong Soo and Kim, Jun and Kim, Sang Jeong},
year = {2014},
pmid = {24121088},
note = {Publisher: Elsevier Inc.
ISBN: 1053-8119},
keywords = {FDG micro-PET, Multivariate pattern analysis, Pain, Resting brain metabolism},
pages = {311--316},
}
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Here, we hypothesized that the induction of neuropathic pain alters distributed patterns of the resting-state brain activity in animal models, and capturing the altered pattern would enable identification of neuropathic pain at the individual level. We acquired micro-positron emission tomography with [18F]fluorodeoxyglucose (FDG micro-PET) images in awake rats with spinal nerve ligation (SNL) and without (sham) (SNL group, n=13; sham group, n=10). Multivariate pattern analysis (MVPA) with linear support vector machine (SVM) successfully identified the brain with SNL (92.31% sensitivity, 90.00% specificity, and 91.30% total accuracy). Predictive brain regions with increased metabolism were mainly located in prefrontal-limbic-brainstem areas including the anterior olfactory nucleus (AON), insular cortex (IC), piriform cortex (PC), septal area (SA), basal forebrain/preoptic area (BF/POA), amygdala (AMY), hypothalamus (HT), rostral ventromedial medulla (RVM) and the ventral midbrain (VMB). In contrast, predictive regions with decreased metabolism were observed in widespread cortical areas including secondary somatosensory cortex (S2), occipital cortex (OC), temporal cortex (TC), retrosplenial cortex (RSC), and the cerebellum (CBL). We also applied the univariate approach and obtained reduced prediction performance compared to MVPA. 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We acquired micro-positron emission tomography with [18F]fluorodeoxyglucose (FDG micro-PET) images in awake rats with spinal nerve ligation (SNL) and without (sham) (SNL group, n=13; sham group, n=10). Multivariate pattern analysis (MVPA) with linear support vector machine (SVM) successfully identified the brain with SNL (92.31\\% sensitivity, 90.00\\% specificity, and 91.30\\% total accuracy). Predictive brain regions with increased metabolism were mainly located in prefrontal-limbic-brainstem areas including the anterior olfactory nucleus (AON), insular cortex (IC), piriform cortex (PC), septal area (SA), basal forebrain/preoptic area (BF/POA), amygdala (AMY), hypothalamus (HT), rostral ventromedial medulla (RVM) and the ventral midbrain (VMB). 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