scholarly journals Classification and characterisation of brain network changes in chronic back pain: a multicenter study

2017 ◽  
Author(s):  
Hiroaki Mano ◽  
Gopal Kotecha ◽  
Kenji Leibnitz ◽  
Takashi Matsubara ◽  
Aya Nakae ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Back Pain ◽  
Sensorimotor Cortex ◽  
Network Architecture ◽  
Cross Validation ◽  
Chronic Back Pain ◽  
Brain Network ◽  
Support Vector ◽  
Data Set ◽  
Pain Patients

AbstractChronic pain is a common and often disabling condition, and is thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Here, we investigated brain network architecture using resting state fMRI data collected from chronic back pain patients in UK and Japan (41 patients, 56 controls). Using a machine learning approach (support vector machine), we found that brain network patterns reliably classified chronic pain patients in a third, independent open data set with an accuracy of 63%, whilst 68% was attained in cross validation of all data. We then developed a deep learning classifier using a conditional variational autoencoder, which also yield yielded 63% generalisation and 68% cross-validation accuracy. Given the existence of reliable network changes, we next studied the graph topology of the network, and found consistent evidence of hub disruption based on clustering and betweenness centrality of brain nodes in pain patients. To examine this in more detail, we developed a multislice modularity algorithm to identify a consensus pattern of modular reorganisation of brain nodes across the entire data set. This revealed evidence of significant changes in the modular identity of several brain regions, most notably including broad regions of bilateral sensorimotor cortex, subregions of which also contributed to classifier performance. These results provide evidence of consistent and characteristic brain network changes in chronic pain, and highlight extensive reorganisaton of the network architecture of sensorimotor cortex.

scholarly journals Classification and characterisation of brain network changes in chronic back pain: A multicenter study

2018 ◽  
Vol 3 ◽  
pp. 19
Author(s):  
Hiroaki Mano ◽  
Gopal Kotecha ◽  
Kenji Leibnitz ◽  
Takashi Matsubara ◽  
Aya Nakae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Pain ◽  
Deep Learning ◽  
Back Pain ◽  
Sensorimotor Cortex ◽  
Network Architecture ◽  
Chronic Back Pain ◽  
Open Data ◽  
Brain Network

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

scholarly journals Classification and characterisation of brain network changes in chronic back pain: A multicenter study

2018 ◽  
Vol 3 ◽  
pp. 19 ◽  
Author(s):  
Hiroaki Mano ◽  
Gopal Kotecha ◽  
Kenji Leibnitz ◽  
Takashi Matsubara ◽  
Christian Sprenger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Pain ◽  
Deep Learning ◽  
Back Pain ◽  
Sensorimotor Cortex ◽  
Network Architecture ◽  
Chronic Back Pain ◽  
Open Data ◽  
Brain Network

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

The periaqueductal gray and descending pain modulation: why should we study them and what role do they play in chronic pain?

2015 ◽  
Vol 114 (4) ◽  
pp. 2080-2083 ◽  
Author(s):  
Kasey S. Hemington ◽  
Marie-Andrée Coulombe
Keyword(s):  
Chronic Pain ◽  
Back Pain ◽  
Functional Connectivity ◽  
Recent Literature ◽  
Chronic Back Pain ◽  
Periaqueductal Gray ◽  
Pain Modulation ◽  
Pain Research ◽  
Descending Pain Modulation ◽  
Pain Patients

In this Neuro Forum we discuss the significance of a recent study by Yu et al. ( Neuroimage Clin 6: 100–108, 2014). The authors examined functional connectivity of a key node of the descending pain modulation pathway, the periaqueductal gray (PAG), in chronic back pain patients. Altered PAG connectivity to pain-related regions was found; we place results within the context of recent literature and emphasize the importance of understanding the descending component of pain in pain research.

scholarly journals Loss of nucleus accumbens low-frequency fluctuations is a signature of chronic pain

2020 ◽  
Vol 117 (18) ◽  
pp. 10015-10023 ◽  
Author(s):  
Meena M. Makary ◽  
Pablo Polosecki ◽  
Guillermo A. Cecchi ◽  
Ivan E. DeAraujo ◽  
Daniel S. Barron ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Low Back Pain ◽  
Back Pain ◽  
Nucleus Accumbens ◽  
Chronic Low Back Pain ◽  
Chronic Back Pain ◽  
Chronic Phase ◽  
Anterior Cingulate ◽  
Low Back ◽  
Pain Patients

Chronic pain is a highly prevalent disease with poorly understood pathophysiology. In particular, the brain mechanisms mediating the transition from acute to chronic pain remain largely unknown. Here, we identify a subcortical signature of back pain. Specifically, subacute back pain patients who are at risk for developing chronic pain exhibit a smaller nucleus accumbens volume, which persists in the chronic phase, compared to healthy controls. The smaller accumbens volume was also observed in a separate cohort of chronic low-back pain patients and was associated with dynamic changes in functional connectivity. At baseline, subacute back pain patients showed altered local nucleus accumbens connectivity between putative shell and core, irrespective of the risk of transition to chronic pain. At follow-up, connectivity changes were observed between nucleus accumbens and rostral anterior cingulate cortex in the patients with persistent pain. Analysis of the power spectral density of nucleus accumbens resting-state activity in the subacute and chronic back pain patients revealed loss of power in the slow-5 frequency band (0.01 to 0.027 Hz) which developed only in the chronic phase of pain. This loss of power was reproducible across two cohorts of chronic low-back pain patients obtained from different sites and accurately classified chronic low-back pain patients in two additional independent datasets. Our results provide evidence that lower nucleus accumbens volume confers risk for developing chronic pain and altered nucleus accumbens activity is a signature of the state of chronic pain.

scholarly journals Identification of traits and functional connectivity-based neuropsychotypes of chronic pain

2018 ◽  
Author(s):  
Etienne Vachon-Presseau ◽  
Sara E. Berger ◽  
Taha B. Abdullah ◽  
James W. Griffith ◽  
Thomas J. Schnitzer ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Socioeconomic Status ◽  
Back Pain ◽  
Cross Validation ◽  
Chronic Back Pain ◽  
Personality Factors ◽  
Network Analyses ◽  
Pain Affect ◽  
Validation Procedure ◽  
Clinical Conditions

AbstractPsychological and personality factors, socioeconomic status, and brain properties all contribute to chronic pain but have essentially been studied independently. Here, we administered a broad battery of questionnaires to patients with chronic back pain (CBP). Clustering and network analyses revealed four orthogonal dimensions accounting for 60% of the variance, and defining chronic pain traits. Two of these traits – Pain-trait and Emote-trait - were related to back pain characteristics and could be predicted from distinct distributed functional networks in a cross-validation procedure, identifying neurotraits. These neurotraits were relatively stable in time and segregated CBP patients into subtypes showing distinct traits, pain affect, pain qualities, and socioeconomic status (neuropsychotypes). The results unravel the trait space of chronic pain leading to reliable categorization of patients into distinct types. The approach provides metrics aiming at unifying the psychology and the neurophysiology of chronic pain across diverse clinical conditions, and promotes prognostics and individualized therapeutics.

scholarly journals Chronic Pain Patients Exhibit Individually Unique Cortical Signatures of Pain

2020 ◽  
Author(s):  
Astrid Mayr ◽  
Pauline Jahn ◽  
Anne Stankewitz ◽  
Bettina Deak ◽  
Anderson Winkler ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Back Pain ◽  
Pain Intensity ◽  
Chronic Back Pain ◽  
Anterior Insula ◽  
Individual Level ◽  
Intensity Changes ◽  
Complex Picture ◽  
The Individual ◽  
Pain Patients

AbstractWe investigated how the trajectory of pain patients’ ongoing and fluctuating pain is encoded in the brain. In repeated fMRI sessions, 20 chronic back pain patients and 20 chronic migraineurs were asked to continuously rate the intensity of their endogenous pain. Linear mixed effects models were used to disentangle cortical processes related to pain intensity and to pain intensity changes. We found that the intensity of pain in chronic back pain patients is encoded in the anterior insula, the frontal operculum, and the pons; the change of pain of chronic back pain and chronic migraine patients is mainly encoded in the anterior insula. At the individual level, we identified a more complex picture where each patient exhibited their own signature of endogenous pain encoding. The diversity of the individual cortical signatures of chronic pain encoding results adds to the understanding of chronic pain as a complex and multifaceted disease.

scholarly journals Individually Unique Dynamics of Cortical Connectivity Reflect the Ongoing Intensity of Chronic Pain

2021 ◽  
Author(s):  
Astrid Mayr ◽  
Pauline Jahn ◽  
Bettina Deak ◽  
Anne Stankewitz ◽  
Vasudev Devulapally ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Back Pain ◽  
Pain Intensity ◽  
Chronic Back Pain ◽  
Personalised Medicine ◽  
Brain Regions ◽  
Cortical Connectivity ◽  
Wide Range ◽  
The Individual ◽  
Pain Patients

Background. Chronic pain diseases are characterised by an ongoing and fluctuating endogenous pain, yet it remains to be elucidated how this is reflected by the dynamics of ongoing functional cortical connections. The present study addresses this disparity by taking the individual perspective of pain patients into account, which is the varying intensity of endogenous pain. Methods. To this end, we investigated the cortical encoding of 20 chronic back pain patients and 20 chronic migraineurs in four repeated fMRI sessions. During the recording, the patients were asked to continuously rate their pain intensity. A brain parcellation approach subdivided the whole brain into 408 regions. A 10 s sliding-window connectivity analysis computed the pair-wise and time-varying connectivity between all brain regions across the entire recording period. Linear mixed effects models were fitted for each pair of brain regions to explore the relationship between cortical connectivity and the observed trajectory of the patients' fluctuating endogenous pain. Results. Two pain processing entities were taken into account: pain intensity (high, middle, low pain) and the direction of pain intensity changes (rising vs. falling pain). Overall, we found that periods of high and increasing pain were predominantly related to low cortical connectivity. For chronic back pain this applies to the pain intensity-related connectivity for limbic and cingulate areas, and for the precuneus. The change of pain intensity was subserved by connections in left parietal opercular regions, right insular regions, as well as large parts of the parietal, cingular and motor cortices. The change of pain intensity direction in chronic migraine was reflected by decreasing connectivity between the anterior insular cortex and orbitofrontal areas, as well as between the PCC and frontal and ACC regions. Conclusions. Interestingly, the group results were not mirrored by the individual patterns of pain-related connectivity, which is suggested to deny the idea of a common neuronal core problem for chronic pain diseases. In a similar vein, our findings are supported by the experience of clinicians, who encounter patients with a unique composition of characteristics: personality traits, various combinations of symptoms, and a wide range of individual responses to treatment. The diversity of the individual cortical signatures of chronic pain encoding results adds to the understanding of chronic pain as a complex and multifaceted disease. The present findings support recent developments for more personalised medicine.

scholarly journals Functional Connectivity Alterations: Novel Therapy and Future Implications in Chronic Pain Management

2018 ◽  
Vol 1 (21;1) ◽  
pp. E207-E214 ◽  
Author(s):  
Dr. Stephen Thorp
Keyword(s):  
Chronic Pain ◽  
Back Pain ◽  
Pain Management ◽  
Functional Connectivity ◽  
Diabetic Neuropathy ◽  
Chronic Back Pain ◽  
Brain Activity ◽  
Pain Syndrome ◽  
Pain Patients ◽  
The Brain

Background: Chronic pain is a major public health problem resulting in physical and emotional pain for individuals and families, loss of productivity, and an annual cost of billions of dollars. The lack of objective measures available to aid in diagnosis and evaluation of therapies for chronic pain continues to be a challenge for the clinician. Objectives: Functional magnetic resonance imaging (fMRI) is an imaging technique that can establish regional areas of interest and examine synchronous neuronal activity in functionally related but anatomically distinct regions of the brain, known as functional connectivity. Study Design: The present investigation examines changes in functional connectivity in 4 common pain syndromes: chronic back pain (CBP), fibromyalgia, diabetic neuropathy, and complex regional pain syndrome (CRPS). Setting: This is a review of the current understanding of functional connectivity. Methods: Utilizing functional imaging, patients with these conditions have been shown to have significant structural and functional differences when compared to healthy controls. Results: Functional connectivity, therefore, has the potential to assist in diagnostic classification of different pain conditions, predict individual responses to specific therapeutic interventions, and serve as a gateway for personalized medicine. Indirect activation of brain activity can be seen by the blood flow to the brain at specific sites, with chronic pain patients having increased brain activity. Limitations: The present investigation is limited in that few studies have examined this relatively new modality. Conclusions: Knowing and observing the brain’s activity as related to pain gives pain patients an opportunity to decrease pain-related brain activity and decrease severe chronic pain. This modality can be used along with interventional pain management techniques in order to provide optimum pain relief. Key words: Functional connectivity, fMRI, chronic pain, chronic back pain, fibromyalgia, diabetic neuropathy, chronic regional pain syndrome

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