Summary

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

Published: January 05, 2024
doi:

Summary

The current study is a randomized, placebo-controlled trial to determine the efficacy of cranial electrical stimulation (CES) for improving pain and function in fibromyalgia and further develop resting functional connectivity magnetic resonance imaging (rs-fcMRI) as a clinical tool to assess the neural correlates and mechanisms of chronic pain and analgesic response.

Abstract

Fibromyalgia is a chronic pain syndrome that presents with a constellation of broad symptoms, including decreased physical function, fatigue, cognitive disturbances, and other somatic complaints. Available therapies are often insufficient in treating symptoms, with inadequate pain control commonly leading to opioid usage for attempted management. Cranial electrical stimulation (CES) is a promising non-pharmacologic treatment option for pain conditions that uses pulsed electrical current stimulation to modify brain function via transcutaneous electrodes. These neural mechanisms and the applications of CES in fibromyalgia symptom relief require further exploration.

A total of 50 participants from the Atlanta Veterans Affairs Healthcare System (VAHCS) diagnosed with fibromyalgia were enrolled and then block-randomized into either a placebo plus standard therapy or active CES plus standard therapy group. Baseline assessments were obtained prior to the start of treatment. Both interventions occurred over 12 weeks, and participants were assessed at 6 weeks and 12 weeks after treatment initiation. The primary outcome investigated whether pain and functional improvements occur with the application of CES. Additionally, baseline and follow-up resting state functional connectivity magnetic resonance imaging (rs-fcMRI) were obtained at the 6-week and 12-week time points to assess for clinical applications of neural connectivity biomarkers and the underlying neural associations related to treatment effects.

This is a randomized, placebo-controlled trial to determine the efficacy of CES for improving pain and function in fibromyalgia and further develop rs-fcMRI as a clinical tool to assess the neural correlates and mechanisms of chronic pain and analgesic response.

Introduction

Of the many existing states of chronic pain, one of the most notoriously difficult diseases to diagnose, clinically assess, and treat is fibromyalgia. Fibromyalgia is a debilitating chronic pain syndrome that involves chronic widespread pain, decreased physical function, fatigue, psycho-emotional and sleep disturbances, and various somatic complaints affecting approximately 2-3% of the general population in the Americas (about 8 million people in the U.S.)1. Diagnosis of the disease is heavily reliant on a patient's understanding of their own symptom profile and pain experience, and without that proper understanding by both clinician and patient of the disease, methods of treatment lose considerable efficacy2. A better definition of fibromyalgia's origins and impact as well as a reliable clinical biomarker to guide fibromyalgia diagnosis and treatment are necessary to best serve all patients.

Even with a confirmed diagnosis, difficulties with the treatment process only grow. As a whole, chronic pain affects more individuals than heart disease, diabetes, and cancer combined. The subjective nature of its assessment places it as a primary driver for the opioid epidemic, especially given the difficulty in discerning incompletely treated physical pain from substance use disorder and drug-seeking behavior3. In 2020, 91,799 drug overdose deaths occurred in the United States (a 30% increase from 2019), and opioids were found to be the main cause of these deaths (74.8% of all 2020 drug overdose deaths)4. Thus, non-pharmacologic alternatives are needed for chronic pain and fibromyalgia treatment to slow the opioid epidemic, which is particularly important in the veteran population where the risk of suicide and opioid use disorder is higher5. Non-pharmacologic and complementary therapies are therefore often used as first-line treatments6.

The search for novel, efficacious fibromyalgia interventions has led many researchers and clinicians to methods of noninvasive brain stimulation, including cranial stimulation. Even though the pathophysiologic mechanisms that result in the development of the disease have not been definitively determined, existing evidence supports the idea that fibromyalgia is a disorder of autonomic nervous system dysfunction and central (i.e., brain and spinal cord) pain processing mechanisms7,8. Stimulation of certain areas of the brain could lead to improved function in those areas of processing. Repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) have been correlated with reductions in pain but have also been associated with activation site scalp irritation, headaches, and inaccessibility outside of treatment facilities9. Noninvasive vagus nerve stimulation (nVNS), which can provide neuromodulation through stimulation over the skin at the neck or at the level of the ear, has the potential for the treatment of chronic pain, and invasive vagus nerve stimulation (VNS) has been shown to improve chronic pain symptoms10. However, neither invasive nor noninvasive VNS have been sufficiently explored in the literature or fully validated for use in fibromyalgia treatment11,12,13,14.

Cranial electrical stimulation (CES) is a non-pharmacological, noninvasive brain stimulation treatment that consists of pulsed, alternating microcurrent (less than 0.5 mA) applied via transcutaneous electrodes placed on the earlobes15. It is remarkably accessible and can be delivered through portable devices used by patients within their own living spaces. In comparison to other cranial stimulation methods, the noninvasive nature and the convenience of patient self-application at home increases the potential of CES as a beneficial option for widespread fibromyalgia treatment use and self-management of pain. It has been cleared by the U.S. Food and Drug Administration (FDA) as a treatment for insomnia, depression, anxiety, and pain15.

The current study evaluates the efficacy of CES as a fibromyalgia treatment modality by comparing active CES (administered by a true study device) versus sham CES (administered by a sham study device). There is some preliminary evidence to support the use of CES in the treatment of pain conditions such as fibromyalgia16,17. A 2001 study of 60 participants randomized to active or sham CES for 3 weeks of daily 60 min sessions revealed a 28% improvement in tender point scores, 27% improvement in general pain scores, and no placebo effect18. CES has not been evaluated in a veteran population, nor has it been adequately evaluated in males with fibromyalgia. A Veterans Affairs (VA)-funded systematic review of CES published in 2018 concluded that evidence is insufficient for CES to have clinically important effects on fibromyalgia, given that most trials had small sample sizes, short durations, and a high risk of bias due to inadequate blinding. However, the review suggests that CES does not cause serious side effects, and there is low-strength evidence to suggest modest benefits in patients with anxiety and depression19. Therefore, further research is warranted regarding the use of this FDA-cleared, low-risk device, particularly in fibromyalgia.

In order to fully evaluate the efficacy, researchers assessed physical fitness alongside neural biomarkers and pain experience. The purpose of treating chronic pain states is to improve physical function. Fibromyalgia is consistently correlated with negative effects on both physical function and patients' perception of their own physical abilities20. Previous studies have utilized simple physical fitness assessments to determine stamina and mobility, such as the 6 Minute Walk Test (6MWT)20,21, Five Time Sit to Stand (5TSTS)20, and various measures of carrying capacity and strength in the context of daily activities22. To account for standard measures while also mitigating the amount of strenuous activity required right before an MRI scan, the study team used the 30-Second Chair Sit Stand Test as a measure of stamina and mobility and both bicep curls and a hand grip test as measures of strength23. The movements required in each of these assessments are very common in everyday activities, so it is a clear measure of how people are physically functioning in their day-to-day lives, both with and without treatment.

Even with subjective pain assessments and physical function measures of efficacy, the mechanisms of CES are not fully understood. Prior neuroimaging studies have sought a better understanding by exploring the direct effect of CES on network connectivity in the brain. Feusner et al.24 found that CES is associated with cortical deactivation for 0.5 Hz and 100 µA stimulation of bilateral frontal, parietal, and posterior midline regions and postulated that frequency of stimulation may have more of an effect than current intensity in relation to cortical deactivation. Their group found significant effects on some but not all nodes of the default mode network (DMN). The authors suggest that based on this data, CES may affect resting state functional connectivity. Fibromyalgia and other chronic pain states have been shown to affect intrinsic brain connectivity in regions associated with pain and perception25,26, so treatments that alter functional connectivity in response could prove to be both beneficial and effective. Further exploration of the longer-term effects of daily treatment in relation to clinical improvement, as well as how deceased activation in the brain relates to previously observed decreases in electroencephalogram frequencies, is needed to further understand the therapeutic mechanism of action27.

Resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) is the neuroimaging method that allows for the observation of these functional connectivity changes. Longitudinal resting state fMRI allows clinicians and researchers to establish a baseline of resting state connectivity and track alterations over time in response to CES treatment methods. It also helps to determine how changes in functional connectivity are correlated to differences in the experience of pain. Initial studies of neuroimaging for fibromyalgia used positron emission tomography (PET) and single-photon-emission computed tomography (SPECT) to examine the brain, but there are issues with both techniques in this regard: SPECT has a lower resolution than PET, and PET scans are invasive, which is not preferable for patients experiencing chronic pain. Functional magnetic resonance imaging (fMRI) scans have greater resolution than SPECT, but they examine brain activity in response to patients' specific actions or perceptions of stimuli28. It is rs-fcMRI scans that can outline functional connectivity between regions of the brain and may be able to determine where and how fibromyalgia exists as well as the best methods of treatment28.

Evaluating the efficacy of non-pharmacologic treatments for pain conditions such as fibromyalgia is of utmost importance both in the current setting of the opioid epidemic and in examining chronic pain as a risk factor for suicide29,30, which is substantially increased among the veteran population. Additionally, the lack of adequate clinical biomarkers for pain is a recognized knowledge gap. Using a combination of behavioral measures and neuroimaging at multiple timepoints to assess treatment response is a novel approach to fibromyalgia evaluation, as is the utilization of auricular CES as a treatment.

The protocol aims to address the gap in fibromyalgia research by investigating the effects of CES on pain and physical function outcomes and evaluating neuroimaging as a tool for predictive and response biomarkers related to the clinical outcomes of CES therapy31.

Protocol

The study was conducted under the approval of the Emory University (IRB 112768) and Atlanta VA Institutional Review Boards (1585632-2; Internal Reference Number: 003) as well as the Atlanta VA R&D Committee (Board Reference Number: 3881). All subjects gave their informed consent for inclusion before they participated in this study. For a visual representation of the study protocol timeline, see Figure 1). <img alt="Figure 1" cl…

Representative Results

In terms of recruitment results, participants were primarily recruited via mailing of recruitment letters and follow-up phone calls based on the outlined regulations of the Atlanta VA Healthcare System. The study team recruited a total of 50 participants, proving the effectiveness of the methods used in meeting the recruitment goal (see Figure 2). The use of the new clinical fibromyalgia diagnostic criteria allowed the study team to properly screen out indivi…

Discussion

The methods of the current study provide not only the possibility of a highly effective treatment modality for fibromyalgia but also the opportunity to improve the diagnostic process of fibromyalgia from the first instance of its pain symptom profile. Use of both active CES and sham CES, with discovery of the type of each individual device being dependent upon serial numbers and a separate key, allowed for blinding of both subjects and researchers until the end of participation, thereby protecting internal validity durin…

Divulgazioni

The authors have nothing to disclose.

Acknowledgements

The authors would like to acknowledge the support of investigators at the Center for Visual and Neurocognitive Rehabilitation, including Dr. Bruce Crosson and Dr. Lisa Krishnamurthy, for their input into the work. The authors also thank  Grace Ingham for her invaluable help in the filming process. This work was supported in part by the United States Department of Veterans Affairs Rehabilitation Research and Development Service Career Development Award IK2 RX003227 (Anna Woodbury) and Center Grant 5I50RX002358. The funder has no role in study design, data collection, management, analysis, interpretation, or reporting.

Materials

3T Siemens MAGNETOM Prisma Scanner Siemens Healthineers N/A From Emory's website: "The Siemens Magnetom Prisma 3T whole-body MR system is equipped with: a state-of-the art gradient system with a maximum (per axis) strength of 80 mT/m and slew rate of 200 T/m/sec
64 independent RF receiver channels capable of 204 receiver connections
a 2-channel RF transmitter. Multiple coils are available, including: a 64-channel head/neck coil with 52 channels for imaging of the head region
a 32-channel head-only coil
a 20-channel head/neck coil with 16 channels for head
spine array coil
flexible chest coil
large and small flexible coil for extremity imaging.
Alpha-Stim AID Kit Electromedical Products International Inc. SKU: 500KIT A total of 50 devices ordered for research purposes.
From the site: "A prescription or order from a licensed healthcare professional is required to purchase this device (within the USA). FDA cleared for anxiety, insomnia and pain only, with approval for depression outside of the United States."
CONN Toolbox v21a16 (RRID:SCR_009550)  Whitfield-Gabrieli and Nieto-Castanon Version v21a16 (RRID:SCR_009550) CONN is an open-source SPM-based cross-platform software for the computation, display, and analysis of functional connectivity Magnetic Resonance Imaging (fcMRI). CONN is used to analyze resting state data (rsfMRI) as well as task-related designs. 
DSI Studio (RRID:SCR_009557)  Fang-Cheng (Frank) Yeh RRID:SCR_009557 DSI Studio is a tractography software tool that maps brain connections and correlates findings with neuropsychological disorders. It is a collective implementation of several diffusion MRI methods, including diffusion tensor imaging (DTI), generalized q-sampling imaging (GQI), q-space diffeomorphic reconstruction (QSDR), diffusion MRI connectometry, and generalized deterministic fiber tracking.
fMRIPrep 20.2.5 (RRID:SCR_016216)  NiPreps (NeuroImaging PREProcessing tools) Version 20.2.5. (RRID:SCR_016216) A functional magnetic resonance imaging (fMRI) data preprocessing pipeline that is designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols and that requires minimal user input, while providing easily interpretable and comprehensive error and output reporting. It performs basic processing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skull-stripping, etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state fMRI, graph theory measures, and surface or volume-based statistics.
MRIQC  NiPreps (NeuroImaging PREProcessing tools) MRIQC extracts no-reference IQMs (image quality metrics) from structural (T1w and T2w) and functional MRI (magnetic resonance imaging) data. (not directly used for analyses)
Sammons Preston Jamar Hydraulic Hand Dynamometer Alpha Med Inc. SKU SAMP5030J1 From the website: Ideal for routine screening of grip strength and initial and ongoing evaluation of clients with hand trauma and dysfunction.
Unit comes with carrying/storage case, certificate of calibration and complete instructions. Warranted for one full year. The warranty does not cover calibration. Latex free.
SPRI 5-Pound Vinyl-Coated Weight SPRI | Amazon N/A Color: (E) Dark Blue | 5-Pound. Appears on Amazon: Dumbbells Hand Weights Set of 2 – Vinyl Coated Exercise & Fitness Dumbbell for Home Gym Equipment Workouts Strength Training Free Weights for Women, Men (1-10 Pound, 12, 15, 18, 20 lb), https://www.amazon.com/stores/SPRI/Weights/page/9D10835A-CFAB-4DA1-BEE9-AE993C6B5BC1
SPRI 8-Pound Vinyl-Coated Weight SPRI | Amazon N/A Color: (H) Black |8-Pound. Appears on Amazon: Dumbbells Hand Weights Set of 2 – Vinyl Coated Exercise & Fitness Dumbbell for Home Gym Equipment Workouts Strength Training Free Weights for Women, Men (1-10 Pound, 12, 15, 18, 20 lb), https://www.amazon.com/stores/SPRI/Weights/page/9D10835A-CFAB-4DA1-BEE9-AE993C6B5BC1

Riferimenti

  1. Heidari, F., Afshari, M., Moosazadeh, M. Prevalence of fibromyalgia in general population and patients, a systematic review and meta-analysis. Rheumatology International. 37 (9), 1527-1539 (2017).
  2. Dennis, N. L., Larkin, M., Derbyshire, S. W. G. A giant mess’ – making sense of complexity in the accounts of people with fibromyalgia. British Journal of Health Psychology. 18 (4), 763-781 (2013).
  3. Woodbury, A. Opioids for nonmalignant chronic pain. AMA Journal of Ethics. 17 (3), 202-208 (2015).
  4. Hedegaard, H., Minino, A. M., Spencer, M. R., Warner, M. Drug overdose deaths in the United States, 1999-2020. NCHS Data Brief. 428, 1-8 (2021).
  5. Department of Veterans Affairs. Opioid prescribing to high-risk veterans receiving VA purchased care. Office of Healthcare Inspections. , (2017).
  6. Perry, R., Leach, V., Davies, P., Penfold, C., Ness, A., Churchill, R. An overview of systematic reviews of complementary and alternative therapies for fibromyalgia using both AMSTAR and ROBIS as quality assessment tools. Systematic Reviews. 6 (1), 97 (2017).
  7. Martinez-Lavin, M., Hermosillo, A. G. Dysautonomia in Gulf War syndrome and in fibromyalgia. The American Journal of Medicine. 118 (4), 446 (2005).
  8. Petersel, D. L., Dror, V., Cheung, R. Central amplification and fibromyalgia: disorder of pain processing. Journal of Neuroscience Research. 89 (1), 29-34 (2011).
  9. Marlow, N. M., Bonilha, H. S., Short, E. B. Efficacy of transcranial direct current stimulation and repetitive transcranial magnetic stimulation for treating fibromyalgia syndrome: A systematic review. Pain Practice. 13 (2), 131-145 (2013).
  10. Molero-Chamizo, A., et al. Noninvasive transcutaneous vagus nerve stimulation for the treatment of fibromyalgia symptoms: A study protocol. Brain sciences. 12 (1), 95 (2022).
  11. Cimpianu, C. L., et al. Vagus nerve stimulation in psychiatry: A systematic review of the available evidence. Journal of Neural Transmission. 124 (1), 145-158 (2017).
  12. Napadow, V., et al. Evoked pain analgesia in chronic pelvic pain patients using respiratory-gated auricular vagal afferent nerve stimulation. Pain Medicine (Malden, Mass). 13 (6), 777-789 (2012).
  13. Zhang, Y., et al. Transcutaneous auricular vagus nerve stimulation (taVNS) for migraine: an fMRI study. Regional Anesthesia and Pain Medicine. 46 (2), 145-150 (2021).
  14. Tassorelli, C., et al. Noninvasive vagus nerve stimulation as acute therapy for migraine: The randomized PRESTO study. Neurology. 91 (4), e364-e373 (2018).
  15. NBC4 Washington – Electrotherapy Device Treats Anxiety, Insomnia, Depression. Alpha-Stim Available from: https://alpha-stim.com/blog/nbc4-washington-electrotherapy-device-treats-anxiety-insomnia-depression/ (2021)
  16. Taylor, A. G., Anderson, J. G., Riedel, S. L., Lewis, J. E., Bourguignon, C. A randomized, controlled, double-blind pilot study of the effects of cranial electrical stimulation on activity in brain pain processing regions in individuals with fibromyalgia. Explore (NY). 9 (1), 32-40 (2013).
  17. Taylor, A. G., Anderson, J. G., Riedel, S. L., Lewis, J. E., Kinser, P. A., Bourguignon, C. Cranial electrical stimulation improves symptoms and functional status in individuals with fibromyalgia. Pain Management Nursing. 14 (4), 327-335 (2013).
  18. Lichtbroun, A. S., Raicer, M. M., Smith, R. B. The treatment of fibromyalgia with cranial electrotherapy stimulation. Journal of Clinical Rheumatology. 7 (2), 72-78 (2001).
  19. Shekelle, P. G., Cook, I. A., Miake-Lye, I. M., Booth, M. S., Beroes, J. M., Mak, S. Benefits and harms of cranial electrical stimulation for chronic painful conditions, depression, anxiety, and insomnia: A systematic review. Annals of Internal Medicine. 168 (6), 414-421 (2018).
  20. Dailey, D. L., et al. Perceived function and physical performance are associated with pain and fatigue in women with fibromyalgia. Arthritis Research & Therapy. 18, 68 (2016).
  21. Gowans, S. E., deHueck, A., Voss, S., Silaj, A., Abbey, S. E., Reynolds, W. J. Effect of a randomized, controlled trial of exercise on mood and physical function in individuals with fibromyalgia. Arthritis & Rheumatism. 45 (6), 519-529 (2001).
  22. Jones, J., Rutledge, D. N., Jones, K. D., Matallana, L., Rooks, D. S. Self-Assessed physical function levels of women with fibromyalgia: A national survey. Women’s Health Issues. 18 (5), 406-412 (2008).
  23. Rikli, R. E., Jones, C. J. Development and validation of criterion-referenced clinically relevant fitness standards for maintaining physical independence in later years. The Gerontologist. 53 (2), 255-267 (2013).
  24. Feusner, J. D., et al. Effects of cranial electrotherapy stimulation on resting state brain activity. Brain and Behavior. 2 (3), 211-220 (2012).
  25. Harris, R. E., et al. Pregabalin rectifies aberrant brain chemistry, connectivity, and functional response in chronic pain patients. Anesthesiology. 119 (6), 1453 (2013).
  26. Napadow, V., Harris, R. E. What has functional connectivity and chemical neuroimaging in fibromyalgia taught us about the mechanisms and management of ‘centralized’ pain. Arthritis Research & Therapy. 16 (5), 425 (2014).
  27. Schroeder, M. J., Barr, R. E. Quantitative analysis of the electroencephalogram during cranial electrotherapy stimulation. Clinical Neurophysiology. 112 (11), 2075-2083 (2001).
  28. Cordes, D., et al. Mapping functionally related regions of brain with functional connectivity MR imaging. American Journal of Neuroradiology. 21 (9), 1636 (2000).
  29. Hassett, A. L., Aquino, J. K., Ilgen, M. A. The risk of suicide mortality in chronic pain patients. Current Pain and Headache Reports. 18 (8), 436 (2014).
  30. Stenager, E., Christiansen, E., Handberg, G., Jensen, B. Suicide attempts in chronic pain patients. A register-based study. Scandinavian Journal of Pain. 5 (1), 4-7 (2014).
  31. Woodbury, A., et al. Feasibility of auricular field stimulation in fibromyalgia: Evaluation by functional magnetic resonance imaging, randomized trial. Pain Medicine. 22 (3), 715-726 (2021).
  32. Wolfe, F., et al. Revisions to the 2010/2011 fibromyalgia diagnostic criteria. Seminars in Arthritis and Rheumatism. 46 (3), 319-329 (2016).
  33. Polomano, R. C., et al. Psychometric testing of the defense and veterans pain rating scale (DVPRS): A new pain scale for military population. Pain Medicine. 17 (8), 1505-1519 (2016).
  34. Electromedical Products International, Inc. Scientific and clinical literature examination for the Alpha-Stim M microcurrent and cranial electrotherapy stimulator. Electromedical Products International, Inc. , (2016).
  35. Lein, D. H., Alotaibi, M., Almutairi, M., Singh, H. Normative reference values and validity for the 30-second chair-stand test in healthy young adults. International Journal of Sports Physical Therapy. 17 (5), 907 (2022).
  36. Revicki, D. A., Cook, K. F., Amtmann, D., Harnam, N., Chen, W. H., Keefe, F. J. Exploratory and confirmatory factor analysis of the PROMIS pain quality item bank. Quality of Life Research. 23 (1), 245-255 (2014).
  37. Tustison, N. J., et al. N4ITK: improved N3 bias correction. IEEE Transactions on Medical Imaging. 29 (6), 1310 (2010).
  38. Avants, B. B., Epstein, C. L., Grossman, M., Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis. 12 (1), 26-41 (2008).
  39. Zhang, Y., Brady, M., Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging. 20 (1), 45 (2001).
  40. Dale, A. M., Fischl, B., Sereno, M. I. Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage. 9 (2), 179-194 (1999).
  41. Klein, A., et al. Mindboggling morphometry of human brains. PLoS Computational Biology. 13 (2), 1005350 (2017).
  42. Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R., Collins, D. L. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage. 47, 102 (2009).
  43. Evans, A. C., Janke, A. L., Collins, D. L., Baillet, S. Brain templates and atlases. NeuroImage. 62 (2), 911-922 (2012).
  44. Greve, D. N., Fischl, B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage. 48 (1), 63-72 (2009).
  45. Jenkinson, M., Bannister, P., Brady, M., Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage. 17 (2), 825 (2002).
  46. Cox, R. W., Hyde, J. S. Software tools for analysis and visualization of fMRI data. NMR in Biomedicine. 10 (4-5), 171-178 (1997).
  47. Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., Beckmann, C. F. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage. 112, 267-277 (2015).
  48. Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E. Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage. 84, 320-341 (2014).
  49. Behzadi, Y., Restom, K., Liau, J., Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage. 37 (1), 90-101 (2007).
  50. Satterthwaite, T. D., et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage. 64, 240-256 (2013).
  51. Lanczos, C. Evaluation of noisy data. Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis. 1 (1), (1964).
  52. Oscar, E., et al. fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods. 16, 111-116 (2019).
  53. Oscar, E., et al. FMRIPrep. Software. Zenodo. , (2018).
  54. Gorgolewski, K. J., et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics. 5, 13 (2011).
  55. Gorgolewski, K. J., et al. Nipype. Software. Zenodo. , (2018).
  56. Abraham, A., et al. Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics. 8, 14 (2014).
  57. Yeh, F. -. C., Badre, D., Verstynen, T. Connectometry: A statistical approach harnessing the analytical potential of the local connectome. NeuroImage. 125 (2016), 162-171 (2015).
  58. Andersson, J. L. R., Skare, S., Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage. 20 (2), 870-888 (2003).
  59. Smith, S. M., et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage. 23, 208-219 (2004).
  60. Andersson, J. L. R., Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage. 125, 1063-1078 (2016).
  61. Yeh, F. -. C., Tseng, W. -. Y. I. NTU-90: a high angular resolution brain atlas constructed by -q-space diffeomorphic reconstruction. Neuroimage. 58 (1), 91-99 (2011).
  62. Nieto-Castanon, A. . Cluster-Level Inferences. Handbook of Functional Connectivity Magnetic Resonance Imaging Methods in CONN. , (2020).
  63. Hemington, K. S., Wu, Q., Kucyi, A., Inman, R. D., Davis, K. D. Abnormal cross-network functional connectivity in chronic pain and its association with clinical symptoms. Brain Structure & Function. 221 (8), 4203-4219 (2016).
  64. Ichesco, E., et al. Altered resting state connectivity of the insular cortex in individuals with fibromyalgia. Journal of Pain. 15 (8), 815-826 (2014).
  65. Kim, J., et al. The somatosensory link in fibromyalgia: functional connectivity of the primary somatosensory cortex is altered by sustained pain and is associated with clinical/autonomic dysfunction. Arthritis & Rheumatology. 67 (5), 1395-1405 (2015).
  66. Napadow, V., LaCount, L., Park, K., As-Sanie, S., Clauw, D. J., Harris, R. E. Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity. Arthritis and Rheumatism. 62 (8), 2545-2555 (2010).
  67. Napadow, V., Kim, J., Clauw, D. J., Harris, R. E. Decreased intrinsic brain connectivity is associated with reduced clinical pain in fibromyalgia. Arthritis and Rheumatism. 64 (7), 2398-2403 (2012).
  68. Puiu, T., et al. Association of alterations in gray matter volume with reduced evoked-pain connectivity following short-term administration of pregabalin in patients with fibromyalgia. Arthritis & Rheumatology. 68 (6), 1511-1521 (2016).
  69. Fallon, N., Chiu, Y., Nurmikko, T., Stancak, A. Functional Connectivity with the default mode network is altered in fibromyalgia patients. PLoS One. 11 (7), 0159198 (2016).
  70. Wang, Y., Kang, J., Kemmer, P. B., Guo, Y. An efficient and reliable statistical method for estimating functional connectivity in large scale brain networks using partial correlation. Frontiers in Neuroscience. 10, 123 (2016).
  71. Mease, P. J., et al. Estimation of minimum clinically important difference for pain in fibromyalgia. Arthritis Care and Research (Hoboken). 63 (6), 821-826 (2011).
  72. Bingel, U., et al. Somatotopic organization of human somatosensory cortices for pain: a single trial fMRI study). NeuroImage. 23 (1), 224-232 (2004).
  73. Wager, T. D., et al. Pain in the ACC. Proceedings of the National Academy of Sciences of the United States of America. 113 (18), E2474-E2475 (2016).
  74. Nieto-Castanon, A. . FMRI Denoising Pipeline. Handbook of Functional Connectivity Magnetic Resonance Imaging Methods in CONN. , (2020).
  75. Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., Turner, R. Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine. 35 (3), 346-355 (1996).
  76. Hallquist, M. N., Hwang, K., Luna, B. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. NeuroImage. 82, 208-225 (2013).
  77. Chai, X. J., Nieto-Castanon, A., Ongur, D., Whitfield-Gabrieli, S. Anticorrelations in resting state networks without global signal regression. NeuroImage. 59 (2), 1420-1428 (2012).
  78. Nieto-Castanon, A. . General Linear Model. Handbook of Functional Connectivity Magnetic Resonance Imaging Methods in CONN. , (2020).
  79. Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., Evans, A. C. A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping. 4 (1), 58-73 (1996).
  80. Chumbley, J., Worsley, K., Flandin, G., Friston, K. Topological FDR for neuroimaging. NeuroImage. 49 (4), 3057-3064 (2010).
  81. Page, S. J., Persch, A. C. Recruitment, retention, and blinding in clinical trials. The American Journal of Occupational Therapy. 67 (2), 154-161 (2013).
  82. McGrath, R. E., Mitchell, M., Kim, B. H., Hough, L. Evidence for response bias as a source of error variance in applied assessment. Psychological Bulletin. 136 (3), 450 (2010).
  83. Robinson-Papp, J., George, M. C., Dorfman, D., Simpson, D. M. Barriers to chronic pain measurement: A qualitative study of patient perspectives. Pain Medicine. 16 (7), 1256-1264 (2015).
check_url/it/65790?article_type=t

Play Video

Citazione di questo articolo
Ree, A., Rapsas, B., Denmon, C., Vernon, M., Rauch, S. A., Guo, Y., Cui, X., Stevens, J. S., Krishnamurthy, V., Napadow, V., Turner, J. A., Woodbury, A. A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers. J. Vis. Exp. (203), e65790, doi:10.3791/65790 (2024).

View Video