TY - JOUR
T1 - Multivariate machine learning distinguishes crossnetwork dynamic functional connectivity patterns in state and trait neuropathic pain
AU - Cheng, Joshua C.
AU - Rogachov, Anton
AU - Hemington, Kasey S.
AU - Kucyi, Aaron
AU - Bosma, Rachael L.
AU - Lindquist, Martin A.
AU - Inman, Robert D.
AU - Davis, Karen D.
N1 - Funding Information:
This work was supported by the Canadian Institute of Health Research (operating grant to K.D.D.); Strategy for Patient-Oriented Research (SPOR) funding of the Canadian Chronic Pain Network; and The Mayday Fund. J. Cheng and K. Hemington are recipients of a Canadian Institute of Health Research Doctoral Research Award. A. Kucyi was supported by a Banting fellowship from Canadian Institute of Health Research.
Publisher Copyright:
© 2018 Lippincott Williams and Wilkins. All rights reserved.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Communication within the brain is dynamic. Chronic pain can also be dynamic, with varying intensities experienced over time. Little is known of how brain dynamics are disrupted in chronic pain, or relates to patients pain assessed at various timescales (eg, shortterm state vs long-Term trait). Patients experience pain "traits" indicative of their general condition, but also pain "states" that vary day to day. Here, we used network-based multivariate machine learning to determine how patterns in dynamic and static brain communication are related to different characteristics and timescales of chronic pain. Our models were based on resting-state dynamic functional connectivity (dFC) and static functional connectivity in patients with chronic neuropathic pain (NP) or non-NP. The most prominent networks in the models were the default mode, salience, and executive control networks. We also found that cross-network measures of dFC rather than static functional connectivity were better associated with patients pain, but only in those with NP features. These associations were also more highly and widely associated with measures of trait rather than state pain. Furthermore, greater dynamic connectivity with executive control networks was associated with milder NP, but greater dynamic connectivity with limbic networks was associated with greater NP. Compared with healthy individuals, the dFC features most highly related to trait NP were also more abnormal in patients with greater pain. Our findings indicate that dFC reflects patients overall pain condition (ie, trait pain), not just their current state, and is impacted by complexities in pain features beyond intensity.
AB - Communication within the brain is dynamic. Chronic pain can also be dynamic, with varying intensities experienced over time. Little is known of how brain dynamics are disrupted in chronic pain, or relates to patients pain assessed at various timescales (eg, shortterm state vs long-Term trait). Patients experience pain "traits" indicative of their general condition, but also pain "states" that vary day to day. Here, we used network-based multivariate machine learning to determine how patterns in dynamic and static brain communication are related to different characteristics and timescales of chronic pain. Our models were based on resting-state dynamic functional connectivity (dFC) and static functional connectivity in patients with chronic neuropathic pain (NP) or non-NP. The most prominent networks in the models were the default mode, salience, and executive control networks. We also found that cross-network measures of dFC rather than static functional connectivity were better associated with patients pain, but only in those with NP features. These associations were also more highly and widely associated with measures of trait rather than state pain. Furthermore, greater dynamic connectivity with executive control networks was associated with milder NP, but greater dynamic connectivity with limbic networks was associated with greater NP. Compared with healthy individuals, the dFC features most highly related to trait NP were also more abnormal in patients with greater pain. Our findings indicate that dFC reflects patients overall pain condition (ie, trait pain), not just their current state, and is impacted by complexities in pain features beyond intensity.
KW - Ankylosing spondylitis
KW - Dynamic functional connectivity
KW - Machine learning
KW - Pain
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U2 - 10.1097/j.pain.0000000000001264
DO - 10.1097/j.pain.0000000000001264
M3 - Article
C2 - 29708944
AN - SCOPUS:85060156845
SN - 0304-3959
VL - 159
SP - 1764
EP - 1776
JO - Pain
JF - Pain
IS - 9
ER -