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Changes in resting-state brain networks after cognitive–behavioral therapy for chronic pain

Published online by Cambridge University Press:  12 September 2017

A. Yoshino ,
Y. Okamoto ,
G. Okada ,
M. Takamura ,
N. Ichikawa ,
C. Shibasaki ,
S. Yokoyama ,
M. Doi ,
R. Jinnin  and
H. Yamashita
...Show all authors
A. Yoshino*
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
Y. Okamoto
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
G. Okada
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
M. Takamura
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
N. Ichikawa
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
C. Shibasaki
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
S. Yokoyama
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
M. Doi
Affiliation:
Department of Dental Anesthesiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
R. Jinnin
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
H. Yamashita
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
M. Horikoshi
Affiliation:
National Center for Cognitive Behavior Therapy and Research, National Center of Neurology and Psychiatry (NCNP), 4-1-1, Ogawahigashicho, Kodaira, Tokyo 187-0031, Japan
S. Yamawaki
Affiliation:
Department of Psychiatry and Neurosciences, Division of Frontier Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
*
Author for correspondence: A. Yoshino, E-mail: yoshino@hiroshima-u.ac.jp
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Abstract

Background

Cognitive–behavioral therapy (CBT) is thought to be useful for chronic pain, with the pathology of the latter being closely associated with cognitive–emotional components. However, there are few resting-state functional magnetic resonance imaging (R-fMRI) studies. We used the independent component analysis method to examine neural changes after CBT and to assess whether brain regions predict treatment response.

Methods

We performed R-fMRI on a group of 29 chronic pain (somatoform pain disorder) patients and 30 age-matched healthy controls (T1). Patients were enrolled in a weekly 12-session group CBT (T2). We assessed selected regions of interest that exhibited differences in intrinsic connectivity network (ICN) connectivity strength between the patients and controls at T1, and compared T1 and T2. We also examined the correlations between treatment effects and rs-fMRI data.

Results

Abnormal ICN connectivity of the orbitofrontal cortex (OFC) and inferior parietal lobule within the dorsal attention network (DAN) and of the paracentral lobule within the sensorimotor network in patients with chronic pain normalized after CBT. Higher ICN connectivity strength in the OFC indicated greater improvements in pain intensity. Furthermore, ICN connectivity strength in the dorsal posterior cingulate cortex (PCC) within the DAN at T1 was negatively correlated with CBT-related clinical improvements.

Conclusions

We conclude that the OFC is crucial for CBT-related improvement of pain intensity, and that the dorsal PCC activation at pretreatment also plays an important role in improvement of clinical symptoms via CBT.

Keywords

Chronic paincognitive behavioral therapyorbitofrontal cortexposterior cingulate cortexresting-state fMRI
Type
Original Articles
Information
Psychological Medicine , Volume 48 , Issue 7 , May 2018 , pp. 1148 - 1156
Copyright
Copyright © Cambridge University Press 2017 

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References

Anderson, MJ and Robinson, J (2001) Permutation tests for linear models. Australian & New Zealand Journal of Statistics 43, 75–88.Google Scholar
APA (1994) Diagnostic and Statistical Manual of Mental Disorders: DSM-IV. Washington, DC: Amer Psychiatric Pub Inc.Google Scholar
Baker, KS, Georgiou-Karistianis, N, Gibson, SJ and Giummarra, MJ (2017) Optimizing cognitive function in persons With chronic pain. The Clinical Journal of Pain 33, 462–472.CrossRefGoogle ScholarPubMed
Beck, A, Brown, G and Steer, R (1996) Beck Depression Inventory-II Manual. San Antonio: Psychological Corporation.Google Scholar
Calhoun, V, Adali, T, Pearlson, G and Pekar, J (2001) A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping 14, 140–151.Google Scholar
Catley, MJ, O'Connell, NE, Berryman, C, Ayhan, FF and Moseley, GL (2014) Is tactile acuity altered in people with chronic pain? A systematic review and meta-analysis. Journal of Pain 15, 985–1000.Google Scholar
Chen, H-J, Chen, Q-F, Liu, J and Shi, H-B (2016) Aberrant salience network and its functional coupling with default and executive networks in minimal hepatic encephalopathy: a resting-state fMRI study. Scientific Reports 6, 27092.Google Scholar
Chou, K-H, Yang, F-C, Fuh, J-L, Kuo, C-Y, Wang, Y-H, Lirng, J-F et al. (2016) Bout-associated intrinsic functional network changes in cluster headache: a longitudinal resting-state functional MRI study. Cephalalgia, Epub ahead of print. DOI: 10.1177/0333102416668657.Google ScholarPubMed
Crowther, A, Smoski, MJ, Minkel, J, Moore, T, Gibbs, D, Petty, C et al. (2015) Resting-state connectivity predictors of response to psychotherapy in major depressive disorder. Neuropsychopharmacology 40, 1659–1673.Google Scholar
Dhond, RP, Yeh, C, Park, K, Kettner, N and Napadow, V (2008) Acupuncture modulates resting state connectivity in default and sensorimotor brain networks. Pain 136, 407–418.Google Scholar
First, MB, Spitzer, RL, Gibbon, M and Williams, JB (2012) Structured Clinical Interview for DSM-IV® Axis I Disorders (SCID-I), Clinician Version, Administration Booklet. New York: American Psychiatric Pub.Google Scholar
Fox, MD and Raichle, ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience 8, 700–711.CrossRefGoogle ScholarPubMed
Fox, MD, Snyder, AZ, Vincent, JL, Corbetta, M, Van Essen, DC and Raichle, ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences USA 102, 9673–9678.CrossRefGoogle ScholarPubMed
Hayasaka, S and Nichols, TE (2004) Combining voxel intensity and cluster extent with permutation test framework. NeuroImage 23, 54–63.Google Scholar
Honey, C, Sporns, O, Cammoun, L, Gigandet, X, Thiran, J-P, Meuli, R et al. (2009) Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences USA 106, 2035–2040.Google Scholar
Jacobs, RH, Watkins, ER, Peters, AT, Feldhaus, CG, Barba, A, Carbray, J et al. (2016) Targeting ruminative thinking in adolescents at risk for depressive relapse: rumination-focused cognitive behavior therapy in a pilot randomized controlled trial with resting state fMRI. PLoS ONE 11, e0163952.Google Scholar
Jensen, KB, Kosek, E, Wicksell, R, Kemani, M, Olsson, G, Merle, JV et al. (2012) Cognitive behavioral therapy increases pain-evoked activation of the prefrontal cortex in patients with fibromyalgeia. Pain 153, 1495–1503.CrossRefGoogle Scholar
Juottonen, K, Gockel, M, Silén, T, Hurri, H, Hari, R and Forss, N (2002) Altered central sensorimotor processing in patients with complex regional pain syndrome. Pain 98, 315–323.CrossRefGoogle ScholarPubMed
Kaichi, Y, Okada, G, Takamura, M, Toki, S, Akiyama, Y, Higaki, T et al. (2016) Changes in the regional cerebral blood flow detected by arterial spin labeling after 6-week escitalopram treatment for major depressive disorder. Journal of Affective Disorders 194, 135–143.Google Scholar
Kregel, J, Meeus, M, Malfliet, A, Dolphens, M, Danneels, L, Nijs, J et al. (2015) Structural and functional brain abnormalities in chronic low back pain: a systematic review. Seminars in Arthritis and Rheumatism 45, 229–237.Google Scholar
Kumari, V, Peters, ER, Fannon, D, Antonova, E, Premkumar, P, Anilkumar, AP et al. (2009) Dorsolateral prefrontal cortex activity predicts responsiveness to cognitive–behavioral therapy in schizophrenia. Biological Psychiatry 66, 594–602.Google Scholar
Leech, R and Sharp, DJ (2014) The role of the posterior cingulate cortex in cognition and disease. Brain 137, 12–32.Google Scholar
Lipowski, ZJ (1988) Somatization: the concept and its clinical application. American Journal of Psychiatry 145, 1358–1368.Google Scholar
Loggia, ML, Kim, J, Gollub, RL, Vangel, MG, Kirsch, I, Kong, J et al. (2013) Default mode network connectivity encodes clinical pain: an arterial spin labeling study. Pain 154, 24–33.Google Scholar
Mason, L, Peters, E and Kumari, V (2016) Functional connectivity predictors and mechanisms of cognitive behavioural therapies: a systematic review with recommendations. Australian and New Zealand Journal of Psychiatry 50, 311–321.Google Scholar
Melzack, R (1987) The short-form McGill pain questionnaire. Pain 30, 191–197.Google Scholar
Menon, V (2011) Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences 15, 483–506.Google Scholar
Neugebauer, V, Galhardo, V, Maione, S and Mackey, SC (2009) Forebrain pain mechanisms. Brain Research Reviews 60, 226–242.Google Scholar
Raichle, ME (2011) The restless brain. Brain Connect 1, 3–12.Google Scholar
Seidel, EM, Pfabigan, DM, Hahn, A, Sladky, R, Grahl, A, Paul, K et al. (2015) Uncertainty during pain anticipation: the adaptive value of preparatory processes. Human Brain Mapping 36, 744–755.CrossRefGoogle ScholarPubMed
Seminowicz, DA and Davis, KD (2007) Pain enhances functional connectivity of a brain network evoked by performance of a cognitive task. Journal of Neurophysiology 97, 3651–3659.Google Scholar
Seminowicz, DA, Shpaner, M, Keaser, ML, Krauthamer, GM, Mantegna, J, Dumas, JA et al. (2013) Cognitive-behavioral therapy increases prefrontal cortex gray matter in patients with chronic pain. The Journal of Pain 14, 1573–1584.Google Scholar
Sheline, YI, Price, JL, Yan, Z and Mintun, MA (2010) Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proceedings of the National Academy of Sciences USA 107, 11020–11025.CrossRefGoogle ScholarPubMed
Shpaner, M, Kelly, C, Lieberman, G, Perelman, H, Davis, M, Keefe, FJ et al. (2014) Unlearning chronic pain: a randomized controlled trial to investigate changes in intrinsic brain connectivity following cognitive behavioral therapy. NeuroImage: Clinical 5, 365–376.CrossRefGoogle ScholarPubMed
Simons, LE, Elman, I and Borsook, D (2014) Psychological processing in chronic pain: a neural systems approach. Neuroscience & Biobehavioral Reviews 39, 61–78.Google Scholar
Smith, SM, Fox, PT, Miller, KL, Glahn, DC, Fox, PM, Mackay, CE et al. (2009) Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences USA 106, 13040–13045.Google Scholar
Spielberger, CD (1983) Manual for the State-Trait Anxiety Inventory STAI (Form Y)(‘Self-Evaluation Questionnaire’). Palo Alto, CA: Consulting Psychologists Press.Google Scholar
Spreng, RN, Mar, RA and Kim, AS (2009) The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: a quantitative meta-analysis. Journal of Cognitive Neuroscience 21, 489–510.Google Scholar
Sullivan, MJL, Bishop, SR and Pivik, J (1995) The pain catastrophizing scale: development and validation. Psychological Assessment 7, 524–532.CrossRefGoogle Scholar
Tamura, Y, Okabe, S, Ohnishi, T, Saito, DN, Arai, N, Mochio, S et al. (2004) Effects of 1-Hz repetitive transcranial magnetic stimulation on acute pain induced by capsaicin. Pain 107, 107–115.Google Scholar
Wager, TD, Atlas, LY, Leotti, LA and Rilling, JK (2011) Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. Journal of Neuroscience 31, 439–452.Google Scholar
Williams, A, Eccleston, C and Morley, S (2012) Psychological therapies for the management of chronic pain (excluding headache) in adults. Cochrane Database of Systematic Reviews 11.Google Scholar
Wu, T-H, Tu, C-H, Chao, H-T, Li, W-C, Low, I, Chuang, C-Y et al. (2016) Dynamic changes of functional pain connectome in women with primary dysmenorrhea. Scientific Reports 6, 24543.CrossRefGoogle ScholarPubMed
Yanagisawa, T, Fukuma, R, Seymour, B, Hosomi, K, Kishima, H, Shimizu, T et al. (2016) Induced sensorimotor brain plasticity controls pain in phantom limb patients. Nature Communications 7, 13209.Google Scholar
Yoshino, A, Okamoto, Y, Horikoshi, M, Oshita, K, Nakamura, R, Otsuru, N et al. (2015) Effectiveness of group cognitive behavioral therapy (GCBT) for somatoform pain disorder patients in Japan: a preliminary non-case-control study. Psychiatry and Clinical Neurosciences 69, 763–772.CrossRefGoogle Scholar
Yoshino, A, Okamoto, Y, Kunisato, Y, Yoshimura, S, Jinnin, R, Hayashi, Y et al. (2014) Distinctive spontaneous regional neural activity in patients with somatoform pain disorder: a preliminary resting-state fMRI study. Psychiatry Research: Neuroimaging 221, 246–248.CrossRefGoogle ScholarPubMed
Yoshino, A, Okamoto, Y, Yoshimura, S, Shishida, K, Toki, S, Doi, M et al. (2013) Distinctive neural responses to pain stimuli during induced sadness in patients with somatoform pain disorder: an fMRI study. Neuroimage: Clinical 2, 782–789.CrossRefGoogle ScholarPubMed
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