Can a power law improve prediction of pain recovery trajectory?

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Introduction:

Chronic pain results from complex interactions of different body systems. Time-dependent power laws have been used in physics, biology, and social sciences to identify when predictable output arises from complex systems. Power laws have been used successfully to study nervous system processing for memory, but there has been limited application of a power law describing pain recovery.

Objective:

We investigated whether power laws can be used to characterize pain recovery trajectories.

Methods:

This review consists of empirical examples for an individual with complex regional pain syndrome and prediction of 12-month pain recovery outcomes in a cohort of patients seeking physical therapy for musculoskeletal pain. For each example, mathematical power-law models were fitted to the data.

Results:

This review demonstrated how a time-dependent power law could be used to refine outcome prediction, offer alternate ways to define chronicity, and improve methods for imputing missing data.

Conclusion:

The overall goal of this review was to introduce new conceptual direction to improve understanding of chronic pain development using mathematical approaches successful for other complex systems. Therefore, the primary conclusions are meant to be hypothesis generating only. Future research will determine whether time-dependent power laws have a meaningful role in improving strategies for predicting pain outcomes.

Related collections

Most cited references 15

Record: found
Abstract: found
Article: not found

Central sensitization and LTP: do pain and memory share similar mechanisms?

Ru-Rong Ji, Tatsuro Kohno, Kimberly Moore … (2003)

Synaptic plasticity is fundamental to many neurobiological functions, including memory and pain. Central sensitization refers to the increased synaptic efficacy established in somatosensory neurons in the dorsal horn of the spinal cord following intense peripheral noxious stimuli, tissue injury or nerve damage. This heightened synaptic transmission leads to a reduction in pain threshold, an amplification of pain responses and a spread of pain sensitivity to non-injured areas. In the cortex, LTP - a long-lasting highly localized increase in synaptic strength - is a synaptic substrate for memory and learning. Analysis of the molecular mechanisms underlying the generation and maintenance of central sensitization and LTP indicates that, although there are differences between the synaptic plasticity contributing to memory and pain, there are also striking similarities.

0 comments Cited 280 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

The rise of multiple imputation: a review of the reporting and implementation of the method in medical research

Panteha Hayati Rezvan, Katherine J Lee, Julie A. Simpson (2015)

Background Missing data are common in medical research, which can lead to a loss in statistical power and potentially biased results if not handled appropriately. Multiple imputation (MI) is a statistical method, widely adopted in practice, for dealing with missing data. Many academic journals now emphasise the importance of reporting information regarding missing data and proposed guidelines for documenting the application of MI have been published. This review evaluated the reporting of missing data, the application of MI including the details provided regarding the imputation model, and the frequency of sensitivity analyses within the MI framework in medical research articles. Methods A systematic review of articles published in the Lancet and New England Journal of Medicine between January 2008 and December 2013 in which MI was implemented was carried out. Results We identified 103 papers that used MI, with the number of papers increasing from 11 in 2008 to 26 in 2013. Nearly half of the papers specified the proportion of complete cases or the proportion with missing data by each variable. In the majority of the articles (86%) the imputed variables were specified. Of the 38 papers (37%) that stated the method of imputation, 20 used chained equations, 8 used multivariate normal imputation, and 10 used alternative methods. Very few articles (9%) detailed how they handled non-normally distributed variables during imputation. Thirty-nine papers (38%) stated the variables included in the imputation model. Less than half of the papers (46%) reported the number of imputations, and only two papers compared the distribution of imputed and observed data. Sixty-six papers presented the results from MI as a secondary analysis. Only three articles carried out a sensitivity analysis following MI to assess departures from the missing at random assumption, with details of the sensitivity analyses only provided by one article. Conclusions This review outlined deficiencies in the documenting of missing data and the details provided about imputation. Furthermore, only a few articles performed sensitivity analyses following MI even though this is strongly recommended in guidelines. Authors are encouraged to follow the available guidelines and provide information on missing data and the imputation process. Electronic supplementary material The online version of this article (doi:10.1186/s12874-015-0022-1) contains supplementary material, which is available to authorized users.

0 comments Cited 108 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches

Andreas Klaus, Shan-shan Yu, Dietmar Plenz (2011)

The size distribution of neuronal avalanches in cortical networks has been reported to follow a power law distribution with exponent close to −1.5, which is a reflection of long-range spatial correlations in spontaneous neuronal activity. However, identifying power law scaling in empirical data can be difficult and sometimes controversial. In the present study, we tested the power law hypothesis for neuronal avalanches by using more stringent statistical analyses. In particular, we performed the following steps: (i) analysis of finite-size scaling to identify scale-free dynamics in neuronal avalanches, (ii) model parameter estimation to determine the specific exponent of the power law, and (iii) comparison of the power law to alternative model distributions. Consistent with critical state dynamics, avalanche size distributions exhibited robust scaling behavior in which the maximum avalanche size was limited only by the spatial extent of sampling (“finite size” effect). This scale-free dynamics suggests the power law as a model for the distribution of avalanche sizes. Using both the Kolmogorov-Smirnov statistic and a maximum likelihood approach, we found the slope to be close to −1.5, which is in line with previous reports. Finally, the power law model for neuronal avalanches was compared to the exponential and to various heavy-tail distributions based on the Kolmogorov-Smirnov distance and by using a log-likelihood ratio test. Both the power law distribution without and with exponential cut-off provided significantly better fits to the cluster size distributions in neuronal avalanches than the exponential, the lognormal and the gamma distribution. In summary, our findings strongly support the power law scaling in neuronal avalanches, providing further evidence for critical state dynamics in superficial layers of cortex.

0 comments Cited 83 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): Pain Rep

Journal ID (iso-abbrev): Pain Rep

Journal ID (coden): PAIREP

Journal ID (publisher-id): Painreports

Title: Pain Reports

Publisher: Wolters Kluwer (Philadelphia, PA )

ISSN (Electronic): 2471-2531

Publication date Collection: Jul-Aug 2018

Publication date (Electronic): 13 June 2018

Volume: 3

Issue: 4

Electronic Location Identifier: e657

Affiliations

[a ]Strategy and Innovation Group, Xerox Corporate Research and Technology, Webster, NY, USA [retired]

[b ]Musculoskeletal Research, Duke Clinical Research Institute, Durham, NC, USA

[c ]Clinical Research, Department of Orthopaedic Surgery, Duke University, Durham, NC, USA

Author notes

[* ]Corresponding author. E-mail address: ghartmann@ 123456nc.rr.com (G.C. Hartmann).

Article

Publisher ID: PAINREPORTS-D-17-0088 Accession ID: 00001

DOI: 10.1097/PR9.0000000000000657

PMC ID: 6085144

SO-VID: 01322e2d-e758-4042-aadc-4a63ea17130f

License:

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 17 December 2017

Date revision received : 12 February 2018

Date accepted : 28 March 2018

Custom metadata

OPEN-ACCESS TRUE

Keywords: chronic pain,power law,complex systems

Data availability:

Keywords: chronic pain, power law, complex systems

Comments

Comment on this article

scite_

Cited by 3

See all cited by

Most referenced authors 172

See all reference authors

Can a power law improve prediction of pain recovery trajectory?

Read this article at

Abstract

Introduction:

Objective:

Methods:

Results:

Conclusion:

Related collections

Genomic Prediction

Most cited references 15

Central sensitization and LTP: do pain and memory share similar mechanisms?

The rise of multiple imputation: a review of the reporting and implementation of the method in medical research

Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches

Author and article information

Journal

Affiliations

Author notes

Article

History

Categories

Custom metadata

Comments

Comment on this article

Similar content 53

Cited by 3

Most referenced authors 172