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Table of Contents    
Year : 2022  |  Volume : 70  |  Issue : 4  |  Page : 1344-1360

A Dysfunctional Descending Pain Modulation System in Chronic Nonspecific Low Back Pain: A Systematic Review and ALE Meta-Analysis

1 Department of Physical Medicine and Rehabilitation, R. G. Kar Medical College, Kolkata, West Bengal, India
2 Department of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, New Delhi, India
3 Department of Physiology, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
4 Department of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
5 Department of Electronics and Communication Engineering, Narula Institute of Technology, Kolkata, West Bengal, India

Date of Submission12-Jan-2022
Date of Decision21-Jun-2022
Date of Acceptance30-Jul-2022
Date of Web Publication30-Aug-2022

Correspondence Address:
Gita Handa
Professor, Department of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, New Delhi
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0028-3886.355137

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 » Abstract 

Pain, a physiological protective mechanism, turns into a complex dynamic neural response when it becomes chronic. The role of neuroplastic brain changes is more evident than the peripheral factors in the maintenance, modulation and amplification of chronic low back pain (cLBP). In this background, we summarise the brain changes in cLBP in a coordinate-based activation likelihood estimation (ALE) meta-analysis of previous functional magnetic resonance imaging (fMRI) studies. Databases ('PubMed', 'Scopus' and 'Sleuth') were searched till May 2022 and the activity pattern was noted under the 'without stimulation' and 'with stimulation' groups. A total of 312 studies were selected after removing duplicates. Seventeen (553 cLBP patients, 192 activation foci) studies were fulfilled the eligibility criteria and included in the 'without stimulation' group. Twelve statistically significant clusters are localized in the prefrontal cortex, primary somatosensory cortex, primary motor cortex, parietal cortex, anterior cingulate cortex, caudate, putamen, globus pallidus amygdala, occipital lobe, temporal lobe and associated white matter in this group. Ten studies (353 cLBP patients, 125 activation foci) were selected in the' with stimulation' groups. In this group, seven statistically significant clusters were found in the frontal cortex, orbitofrontal cortex, premotor cortex, parietal cortex, claustrum and insula. These statistically significant clusters indicate a probable imbalance in GABAergic modulation of brain circuits and dysfunction in the descending pain modulation system. This disparity in the pain neuro-matrix is the source of spontaneous and persisting pain in cLBP.

Keywords: Activation likelihood estimation meta-analysis, chronic nonspecific low back pain, descending pain modulation system, functional magnetic resonance imaging, GABAergic circuitry in brain
Key Message:In this meta-analysis, the brain changes in chronic low back pain is summarised. The dysfunction in the descending pain modulating system and a probable imbalance in GABAergic circuits of the brain are the sources of spontaneous and persisting pain.

How to cite this article:
Hazra S, Handa G, Nayak P, Sahu S, Sarkar K, Venkataraman S. A Dysfunctional Descending Pain Modulation System in Chronic Nonspecific Low Back Pain: A Systematic Review and ALE Meta-Analysis. Neurol India 2022;70:1344-60

How to cite this URL:
Hazra S, Handa G, Nayak P, Sahu S, Sarkar K, Venkataraman S. A Dysfunctional Descending Pain Modulation System in Chronic Nonspecific Low Back Pain: A Systematic Review and ALE Meta-Analysis. Neurol India [serial online] 2022 [cited 2023 Dec 7];70:1344-60. Available from:

Pain is a cornerstone protective mechanism from actual or potential tissue damage. The sensory inflow of acute nociceptive pain follows a labelled pathway to the brain. Although the role of the emotional cognitive component is pervasive. It is interesting to note that, the neurophysiology of chronic pain is very different in terms of amplification, maintenance and modulation. The debate is how it becomes chronic and what will be the treatment strategy in chronic pain. Pain has been considered as physiologically specialized since the time of French philosopher Renee Descartes (1644). The specific pain receptors in the body project the information through nerve fibers in specific regions of the brain.[1] However, in chronic pain, the pain can be maintained and modulated in the brain by central sensitization.[2] Therefore, researchers have tried to correlate the pain in chronic nonspecific low back pain (cLBP) with degenerative spinal changes and neuroplastic brain changes. For the correlation, they have used spinal radiological imaging and functional and anatomical brain magnetic resonance imaging to explain the root cause of persisting pain in cLBP.

However, no causal relationship was found between degenerative spinal radiographic findings and cLBP in a systematic review by van Tulder et al.[3] Interestingly, researchers have not found any role of anatomical peripheral changes in the conversion of acute back pain into chronic back pain. As possible risk factors for the development of cLBP, Chou et al.[4] listed the presence of maladaptive pain-coping behaviors, nonorganic signs, functional impairments, general health status, and the presence of psychiatric comorbidities in a systematic review. Even imaging does not appear to affect the overall prognosis in low back pain.[5] With this background, clinical practice guidelines have recommended against routine use of imaging in nonspecific low back pain.[6] Moreover, van Tulder et al., in an evidence-based review, showed that there is insufficient evidence favoring facet joint, epidural, and trigger point injections in low back pain.[7] All these systematic reviews point out to a poor correlation between the cLBP and degenerative spinal changes.

Contrary to this notion, Kregel et al.[8] pointed out the neuroplastic changes in both gray and white matter in a systematic review of functional brain imaging studies in cLBP patients. They also identified that there is increased activation in a few pain-processing brain areas, such as the medial prefrontal cortex (mPFC), cingulate cortex, amygdala, and insula during functional magnetic resonance imaging (fMRI) studies without any stimulus. Increased activation was demonstrated in the mPFC, cingulate cortex, amygdala, insula, primary somatosensory cortex (S1), primary motor cortex (M1), and secondary somatosensory cortex (S2) after painful stimuli or physical maneuvers. In another meta-analysis of 293 patients, Yuan et al.[9] showed a decrease in gray matter volume in the bilateral mPFC, anterior cingulate cortex (ACC), and right orbitofrontal cortex. In another systematic review of structural and functional brain changes in chronic low back pain, the authors have suggested that brain changes corroborate the brain emotional network, rather than the nociceptive pathway.[10]

Approaches toward the evaluation and treatment strategies of cLBP centered on peripheral spinal factors of cLBP have been challenged by this equivocal evidence. Recent evidence points toward neuroplastic brain changes as a primary factor for the persisting pain in cLBP. At the molecular and cellular levels, chronic nociception leads to brain reorganization. These microscopic changes result in the release of excitatory and inhibitory neurochemicals from neurons and glial cells, upregulation of ionotropic and metabotropic receptors by signaling pathways, and alteration in presynaptic and postsynaptic neuronal excitability.[11] The final outcome of these molecular changes leads to long-term potentiation and central sensitization.[12],[13] These neuroplastic changes are reflected in altered functional brain connectivity. Therefore, there is an inevitable need to explore the mechanisms of pain in cLBP to optimize our diagnostic and therapeutic strategies.

In the clinical practice guidelines, cLBP is defined as “pain occurring primarily in the back with no signs of a serious underlying condition (such as cancer, infection, or cauda equina syndrome), spinal stenosis or radiculopathy, or another specific spinal cause (such as vertebral compression fracture or ankylosing spondylitis).”[14] Low back pain of more than 3 months of duration is defined as chronic low back pain.[14] Degenerative changes on imaging of the lumbar spine are usually considered nonspecific, as they correlate poorly with symptoms.[14]

In this context, previous brain imaging studies in cLBP are summarized in this meta-analysis. The gold standard meta-analysis for imaging studies should include the full statistical map of previously published studies by aggregating the effect size at each voxel (unitary three-dimensional [3D] point in 3D image, here in brain MRI).[15] As full statistical maps are rarely available, peak coordinate-based methods are commonly used. This coordinate-based activation likelihood estimation (ALE) analysis assesses the consistency of activation in each voxel.[16] An ALE-based meta-analysis of functional brain imaging studies was carried out to identify the areas activated in cLBP patients.

 » Materials and Methods Top


The protocol of this review is registered in International Prospective Register of Systematic Reviews (PROSPERO) (registration number: CRD42020203007).

Eligibility criteria

Studies with functional whole-brain imaging in patients with cLBP were included. Studies without brain imaging, reviews, animal studies, case reports, studies without full text, non-English language studies, studies without any peer review, studies not including whole-brain analysis, studies with only structural imaging, and studies not mentioning any standard stereotactic space 3D coordinates X, Y, Z, such as Talairach or Montreal Neurological Institute for peak coordinates, were excluded. The selected studies were performed under resting conditions and with or without mechanical, thermal, and pressure stimulations. Previous studies have shown that in chronic low back pain patients, different brain areas are activated in fMRI studies with or without thermal stimulation.[17],[18] Apkarian[19] concluded that it is difficult to interpret the fMRI studies with mechanical and thermal stimulation in spontaneous pain such as cLBP. Therefore, we categorized the fMRI studies into two groups, that is, with stimulation and without stimulation. If any suitable article has reported fMRI data in both the conditions, we have included the peak coordinate separately.

Information source: Search and data item

Literature was searched online in PubMed, Scopus, and Sleuth (BrainMap database). We have also searched the references of relevant studies and review articles in addition. The main keywords and conjunctives used in search were: (Chronic low back pain) AND (Brain OR Brain Activity OR Cortical changes OR Cortex OR Cortical activity OR Synapse OR Synaptic changes OR Sensorimotor processing OR Plasticity) AND (Central Nervous System Sensitization OR Sensitization OR Central sensitivity OR Central hyperexcitability OR Central sensitization OR Pain modulation OR Neural inhibition OR Hyperalgesia OR nociception OR Pain threshold OR Algometry OR Hypersensitivity OR Gray matter OR White matter OR Functional connectivity) AND (MRI OR Magnetic resonance imaging OR fMRI OR Functional magnetic resonance imaging OR PET OR Positron emission tomography OR evoked potential OR NIRS OR fNIRS OR functional near-infrared spectroscopy OR Optical neuroimaging study OR Diffusion tensor imaging OR EEG OR Electroencephalography OR Brain imaging). The search was performed independently by SH and GH and was supervised by GH and SV. All of them have completed speciality training in Physical Medicine and Rehabilitation. They have 6, 4, 27, and 20 years of experience, respectively, in this speciality.

Study selection

In PubMed, Scopus, and Sleuth (BrainMap online database), we found 323, 28,7 and 17 studies, respectively, until May 2022. Additionally, after searching the reference lists of relevant studies and review articles, we found an additional 10 references. After removing the duplicate studies, we found 311 studies. Of these, 128 studies were excluded for the following reasons: not having brain imaging studies, reviews, animal studies, case reports, nonavailability of full text, non-English language reports, and studies that were not peer reviewed. From this collection, 157 studies were excluded for not having whole-brain analysis, reporting structural imaging only, not mentioning standard stereotactic space coordinates (Talairach or Montreal Neurological Institute), and not satisfying the case definition of chronic low back pain.[14] We divided the studies that fulfilled the inclusion criteria into two categories, that is, (1) without lower back stimulation and (2) with lower back stimulation. Finally, 17 studies were included in the systematic review and meta-analysis in the “without stimulation” group. In the second group, 10 studies were included for qualitative and quantitative analyses [Figure 1].
Figure 1: Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram showing the sequence of the literature search and the process of inclusion and exclusion of articles according to the PRISMA statement

Click here to view

Risk of bias assessment

Risk of bias was assessed by the Joanna Briggs Institute critical appraisal checklist for analytical cross-section studies, developed by the Faculty of Health and Medical Sciences at the University of Adelaide, South Australia.[20] It has eight items, and each item has four options for answering, namely, (1) yes, (2) no, (3) unclear, and (4) not applicable. For risk of bias assessment, GH prepared one Google-form according to the Joanna Briggs Institute critical appraisal checklist and shared it with SH and SS. Both of them filled in the form independently. Finally, the responses were checked by GH and SV. For any differences, GH and SV gave their input and decided with consensus of SH, SS, GH, and SV.

ALE meta-analysis

ALE meta-analysis summarizes the coordinates in a voxel-based analysis to determine which regions are consistently activated.[15] It is used to localize the pattern of anatomical brain regions activated in a particular type of task. The null hypothesis for the ALE method is that foci of activations are uniformly spread throughout the whole brain. Therefore, this statistical method is used to assess the activation probabilities for each voxel in the brain. The null hypothesis is rejected when at least one peak coordinate falls within the voxels. In ALE, the Monte Carlo procedure generates n number of peaks at random locations. The peaks are assumed to follow a Gaussian distribution, but the mean and variance are unknown. Primarily, a random mean and standard deviation is considered. Then, the mean and deviation are adjusted to minimize the objective function. In doing so, a distribution is fitted, which is closely matched with the voxel distribution and grouped with similarity measures.[21] The ALE value is calculated taking the union of peak probabilities where the probability is statistically significant. The Z-score is used to standardize the distribution for comparison. As a result, the Z-score has a distribution with a mean of 0 and a standard deviation of 1. Accordingly, this signifies how far the point is from the mean of a data point. We used GingerALE 3.0.2 for ALE meta-analysis, and this is based on the protocol proposed by Eickhoff et al.[16],[22] For the threshold of the ALE map, the P value for the cluster-level familywise error (FWE) was chosen to be <0.05 (corrected), and for cluster formation, the voxel-level forming threshold P value was chosen to be <0.001 (uncorrected).[23],[24] The observed P value is also known as the uncorrected P value. The uncorrected P value can be adjusted as suggested by Bonferroni, and this is known as the corrected P value. We have chosen the threshold for the minimum cluster size as >200 mm3.[23] For visualization of the results, we have used Mango (4.1) by the Research Imaging Institute, UTHSCSA and the anatomical template provided on the GingerALE website (Colin27_T1_seg_MNI.nii, This template was overlaid with the ALE map generated by GingerALE 3.0.2. This ALE estimation and visualization was performed by SH and KS.

 » Results Top

Study selection: A total of 17 and 10 studies were selected among 312 studies for the systematic review of the without stimulation and with stimulation groups, respectively. Relevant information from the included studies is presented in [Table 1]a and [Table 1]b. The following items were included: (1) study, (2) objective, (3) inclusion criteria, (4) exclusion criteria, (5) participant details, and (6) location of the brain where peak activity was reported. The handedness of the patients was reported in the selected studies, and cLBP patients were right-handed.
Table 1:

Click here to view

Risk of bias: We assessed the risk of bias of the eight selected fMRI studies without stimulation [Table 2]a and six fMRI studies with stimulation [Table 2]b in cLBP by the Joanna Briggs Institute critical appraisal checklist.
Table 2:

Click here to view

ALE meta-analysis results

An ALE meta-analysis of a total of 17 and 10 selected studies was performed in the without stimulation and with stimulation groups, respectively. In the without stimulation group, 192 activation foci were considered from a pooled data of 553 patients. In the with stimulation group, 125 activations were identified from a pooled data of 353 patients. The minimum cluster size was chosen as 200 mm3. Statistically significant clusters are described in [Table 3]a and [Table 3]b and [Figure 2]a, [Figure 2]b and [Figure 3]. In the without stimulation group, statistically significant activation was found in both the hemispheres and frontal, parietal, limbic, temporal, occipital, and sublobar regions. In the with stimulation group, a significant laterization was observed and most of the clusters were in the right hemisphere [Figure 2]a, [Figure 2]b. Activation was observed in the frontal lobe, parietal lobe, insula, and claustrum. Both the gray and white matter areas were activated in both the groups. White matter involvement was more common in fMRI studies with stimulation than in those without stimulation (75.3% vs. 36.2%).
Table 3:

Click here to view
Figure 2: (a) Results of the ALE meta-analysis for the studies in the “without stimulation” group. The result is overlaid on the brain anatomical template provided on the GingerALE website (Colin27_T1_seg_MNI.nii, A sliding scale is used to depict the association. Red → blue: strong association → weaker association. Coordinates of the clusters (X, Y, Z) are provided in the MNI coordinate system of the human brain, mm3: area of the cluster in cubic millimeters. Cluster-forming values: threshold of ALE map the P < 0.05 corrected for cluster-level FWE and P < 0.001 voxel-level forming threshold (uncorrected), threshold for minimum cluster size is >200 mm3. (b) Results of the ALE meta-analysis for the studies in the “with stimulation” group. The result is overlaid on the brain anatomical template provided on the GingerALE website (Colin27_T1_seg_MNI.nii, A sliding scale is used to depict the association. Red → blue: strong association → weaker association. Coordinates of the clusters (X, Y, Z) are provided in the MNI coordinate system of the human brain, mm3: area of the cluster in cubic millimeters. Cluster-forming values: threshold of ALE map the P < 0.05 corrected for cluster-level FWE and P < 0.001 voxel-level forming threshold (uncorrected), threshold for minimum cluster size is >200 mm3. ALE = activation likelihood estimation, FEW = familywise error, MNI = Montreal Neurological Institute

Click here to view
Figure 3: Schematic result of ALE meta-analysis for the studies in the “without stimulation” and “with stimulation” groups. The numbers depict the Brodmann areas listed in [Table 3]a and [Table 3]b. ALE = activation likelihood estimation

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 » Discussion and Conclusions Top

The fMRI assesses spatial activity in the entire brain of with stimulation or without stimulation group in cLBP patients. In fMRI, Blood-oxygen-level-dependent imaging (BOLD) MRI signals reflect neuronal activity based on neuronal oxygenation. The activity pattern was different in spontaneous pain and hyperalgesia–allodynia. This is reflected in the finding of the without stimulation and with stimulation group, respectively. In this ALE meta-analysis of 17 fMRI without stimulation studies, we found 12 statistically significant clusters activated in “spontaneous” cLBP. These are distributed in the prefrontal cortex, primary somatosensory cortex, primary motor cortex, parietal cortex, ACC, caudate, putamen, globus pallidus amygdala, occipital lobe, temporal lobe, and associated white matter [Table 3]a, and [Figure 2]a and [Figure 3]. Nine studies have mentioned the diagnostic criteria they have used to define cases [Table 2]a. Other studies included patients diagnosed clinically. Eleven studies mentioned about the confounding factors.

We found seven statistically significant clusters in the ALE meta-analysis of 10 fMRI studies of the with stimulation group in cLBP denoted “hyperalgesia–allodynia.” These were in frontal cortex, orbitofrontal cortex, premotor cortex, parietal cortex, claustrum, and insula [Table 3]b, and [Figure 2]b and [Figure 3]. In this group, eight studies mentioned the diagnostic criteria for case definition and only six studies specified about the confounding factors [Table 2]b.

We divided the brain into four regions according to the pain matrix: (1) sensory–motor regions, (2) cognitive regions, (3) affective regions, and (4) modulatory regions.[52] The significance of these identified clusters in cLBP is discussed in the following sections according to these facets of pain neuro-matrix.

Sensory–Motor region

The sensory discriminative element of pain indicates the intensity, that is, when, where, and how.[53] In addition, the primary somatosensory cortex (S1) processes some epicritic information and the secondary somatosensory cortex (S2) addresses some higher cognitive component of pain.[54] Primary motor cortex involvement in chronic pain is associated with the execution of movement. In a recent meta-analysis, Chang et al.[55] reported the evidence of involving M1 cortex in chronic pain is inconclusive. There is evidence of expansion and shift of cortical areas in S1, S2, M1, ACC, and insular cortex (IC) with chronicity of pain.[56] In the without stimulation group of this study, two statistically significant clusters were found: one in the right and the other one in the left primary somatosensory cortex (BA3) and primary motor cortex (BA4) [Table 3]a, and [Figure 2]a and [Figure 3]. Similarly, two clusters were found in the right and left S2 cortex (BA40) and two clusters were found in the right and left insula of the with stimulation group [Table 3]b. In this context, it is noteworthy that the insular and S1 cortex define the laterality of pain.[57] The involvement of the S2 and IC in the present study in the with stimulation group possibly indicates the involvement of a higher cognitive component in the case of hyperalgesia or allodynia and affective assignment of pain, which may be in relation with the laterality of stimulation. Godinho et al.[58] concluded that relatively late-occurring responses in the right somatosensory, temporo-occipital, and temporal hemispheres are associated with the memory encoding and emotional components of pain. In the present analysis, two clusters were also found in the supplementary motor cortex (Rt BA6) and one in motor association cortex (Rt BA8) in the with stimulation group [Table 3]b, and [Figure 2]b and [Figure 3]. Misra et al.[59] have shown that these areas are connected with pain and motor control. It is also found that stimulation of the motor cortex reduces the intensity of nociception.[60]

Cognitive region

Prefrontal activity is correlated with the cognitive domain of nociception; therefore, it is related to memory, attention, knowledge, and understanding.[54] However, Coghill et al.[61] and Strigo et al.[62] have shown that this area is not directly associated with sensation and affect. The prefrontal cortex (BA 10, 9) is associated with modulating nociception in the “top–down” approach. More precisely, the orbitofrontal cortex (BA10) controls affective perception.[63] Several human and animal studies have documented a time-dependent decrease in gray matter volume in the prefrontal cortex.[64] Among the selected studies for the current analysis, there were four statistically significant clusters in the prefrontal cortex, that is, the left anterior prefrontal cortex (BA10), the left dorsolateral prefrontal cortex, and the mPFC (BA9) [Table 3]a, and [Figure 2]a and [Figure 3] in the without stimulation group. In contrast, we found no significant clusters of activation in the with stimulation group [Table 3]b. Baliki et al.[17] have also documented increased activity in the mPFC (BA 12, 24, 25, 32, 33), and this activity was increased by atrophy of the dorsolateral prefrontal cortex (BA 8, 9, 10, 46). Hashmi et al.[29] also pointed out increased activity in the mPFC amygdala and basal ganglia as a marker of cLBP. Furthermore, in support of these observations, the mPFC and rostral ACC (rACC) have been found to have increased activity in a recent study by Tu et al.[37] In a systematic review, Kregel et al. also supported the involvement of the prefrontal cortex in chronic low back pain.[8]

Affective region

The affective component denotes the “unpleasantness” of pain perception. Activation of corticolimbic circuitry has been postulated to be a risk factor for the development of chronic pain.[54] In this study, activation was found in the left cingulate cortex (rACC) (BA32, BA24, BA30), orbitofrontal cortex (BA10, BA13), left amygdala, bilateral caudate, left putamen, and bilateral globus pallidus. It is evident that activation of the corticolimbic pathway is more than that of mesolimbic pathway as there is no statistically significant activation of the regions like hippocampus, thalamus, and mid brain. Among the selected studies, in the without stimulation group, activation of the left rACC (dorsal ACC [BA32] and ventral ACC [BA 24]) [Table 3]a, and [Figure 2]a and [Figure 3] was documented by Baliki et al.,[17] Hashmi et al.,[29] and Tu et al.[37] Tölle et al.[65] and Zubieta et al.[66] also showed that ACC is more related to the affective component of pain. Orbitofrontal cortex is another region that processes the affective component of pain perception.[67]

The IC has been found to be involved in sensory and affective dimensions of pain. The anterior insula performs as an integration site for multimodal information of pain, including attention, anticipation, and belief.[54] It is also associated with pain intensity and possible pain amplification.[68] It has circuitry connections with the PFC, ACC, amygdala, and descending pain modulation system. Initially, the glutaminergic receptors play a role in the chronification of pain in the IC. Subsequently, the imbalance between the glutaminergic, GABAergic, and dopaminergic pathways contributes to dysfunction in pain processing and modulation.[69] In the with stimulation group, three clusters were found in the IC (BA13) in the right and left lobes, respectively [Table 3]b, and [Figure 2]b and [Figure 3]. The contralateral involvement of the insula in the with stimulation group may be due to the laterality of nociception, as described in the previous section.

In pain processing, circuitries of the central amygdala (CaA) and basolateral amygdala (BLA) complex act in conjunction. The CaA is associated with negative emotional aspects of pain and is named the “nociceptive amygdala.”[70] However, the BLA integrates polymodal sensory information (both noxious and non-noxious) and generates a memory regarding nociception.[54] The amygdala acts as a central component of GABAergic circuitry in the brain and controls the reward–aversion circuitry. It receives sensory information from spinal lamina 1 through the spinoreticular pathway (parabrachial nucleus). The amygdala sends information to the PFC and ACC and controls the descending pain modulating system by periaqueductal gray (PAG) and rostral ventrolateral medulla (RVM).[54] In this meta-analysis, one statistically significant cluster was found in this area in the without stimulation group [Table 3]a, [Figure 2]a and [Figure 3].

In the present analysis, three clusters were noted in the basal ganglia in the without stimulation group (lateral and medial globus pallidus, caudate head, and putamen) and two in the with stimulation group (claustrum) [Table 3]a and [Table 3]b, [Figure 2]a, [Figure 2]b and [Figure 3]. The basal ganglia are associated with motor, associative, and emotional processing of pain.[71] The putamen is concerned with maintaining the somatotopic maps of pain[72] and subjective ratings of pain.[73] The globus pallidus processes the behavioral repertories of pain. It is also documented that opioid and deep brain stimulation to this area produces analgesia.[71] The caudate nucleus encodes stimulus intensity to minimize harm and actuates the avoidance behavior of pain. This area is also responsive to opioid.[71] The claustrum deals with the emotional aspect of pain.[68]

Involvement of temporo-occipital lobe is associated with unpleasantness.[35],[74] One cluster (BA 37 and 19) was found in this area in the without stimulation group [Table 3]a, [Figure 2]a and [Figure 3].

The parietal cortex (BA31, BA23, BA44, BA40) is associated with integration of multisensory information, awareness to the surroundings, and spatial weighting.[75] Angular gyrus (BA39),[67],[76] extrastriate cortex (BA19),[77] and fusiform gyrus (BA37)[50] are involved in pain perception and cognitive processing.

Modulatory region

Electrical stimulation to the PAG produces an antinociceptive effect in the dorsal horn spinal cord circuitry. The PAG output pathway is influenced by the PFC and ACC, and this connection is predominantly GABAergic. In cLBP patients, this descending circuitry to the PAG displays abnormal functional connectivity.[78] The PAG–spinal cord projection routes through the RMV. There are two classes of neurons in RMVs: (1) pronociceptive ON cells (glutaminergic) and (2) antinociceptive OFF cells (GABAergic and enkephalinergic).[79] The PFC also acts as a connecting node in the BLA–PFC–PAG circuitry. Prefrontal deactivation depresses this antinociceptive descending pathway. The final outcome is increased descending facilitation and decreased inhibition (pronociceptive) of the PAG–RMV–spinal cord circuit.[54] This pronociceptive priming of RMV causes a reduction in the threshold of activation of both ON and OFF cells to innocuous stimuli in chronic pain. The failure of compensatory rebalancing and decreased top–down modulation makes the circuitry nocifensive.[79] In the present study, no significant cluster was identified in the descending pain modulatory region in either of the study groups [Table 3]a and [Table 3]b. This is probably due to a decrease in PFC–PAG output.

The white matter involvement was greater in the with stimulation group than in the other group (75.3% vs. 36.2%) [Table 3]a and [Table 3]b. Chronic pain is associated with both “neuropathy” and “gliopathy.” After injury, the glial modulators activate microglia and astrocytes. Activated glial cells release various neuromodulators and ultimately, they induce synaptic and neuronal plasticity.[80],[81] The increase in white matter involvement in the with stimulation group is probably because of central sensitivity. cLBP is also associated with white matter hyperintensity.[82] The authors proposed that glial activation leads to N-methyl-D-aspartate (NMDA) receptor upregulation, glutamate receptor upregulation via glutamate–glutamine shuttle, and increased release of proinflammatory cytokines. All these lead to central sensitization.[11]

In this study, a stringent case definition of cLBP was followed to exclude the heterogenicity and ambiguity of the case definition. This excludes the few experiments included in the previous systematic reviews. This face-off between power and homogeneity reduces the power of this study, which may affect the generalizability of the results.[24] Data from the fMRI studies were extracted manually and were double checked by SH and KS to minimize the chances of error. The selected studies were of cross-sectional study design. The selected studies mentioned about the diagnostic criteria and the confounding factors, except a few. Future meta-analyses combining all the spatiotemporal domains of fMRI studies could be interesting. Additionally, the inclusion of more studies with specific case definitions of cLBP can increase the power of meta-analysis.

The nodes for the circuits are the S1 cortex, rACC (BA 32, 24), prelimbic PFC (BA 10, 9), orbitofrontal (BA 10, 13), parietal cortex, and amygdala. Basal ganglia, parietal cortex (BA31, BA23, BA44, BA40), temporo-occipital lobe (BA 37, 19), angular gyrus (BA39), extrastriate cortex (BA19), fusiform gyrus (BA37), M1, and premotor cortex activation are also noted along with these areas. These areas are involved in the cognitive, affective, and sensory discriminative–efferent responses to pain. Nevertheless, no statistically significant activation was found in the infralimbic area (BA25), periaqueductal, rostral ventral medullary area, or parabrachial area. These areas are associated with the descending pain modulation system. According to the current concept of GABAergic circuitry in chronic pain, the neurons are involved in the sensory–affective component of pain, generation of gamma oscillatory rhythms, and fine-tuned balance between descending facilitation and descending inhibition of nociception via the PAG–RMV–spinal cord axis.[55] The clusters found in this study cover up the GABAergic circuitry [Figure 4]. There is no significant cluster found in the descending pain modulatory region in this meta-analysis. Hence, the fine-tuning balance between descending facilitation and inhibition in this circuit is altered. The resultant dysfunction in recruitment of the descending pain modulation system creates a pronociceptive circuit.
Figure 4: Alteration of brain circuitry in cLBP. Light blue boxes: anatomical structures, purple boxes: physiological events, yellow boxes: end result of the pathways. Peripheral afferent pain sensation reaches the spinal cord via the dorsal root ganglion. It travels through the spinothalamic tract and reaches the somatosensory cortex. In the somatosensory cortex, the GABAergic neurons produce desynchronized ɣ band oscillation. This can also be produced in the case of allodynia. The net result is nociception. On the other hand, the amygdala is activated via nociceptive stimuli (via the parabrachial nucleus) and stress. The net output of the central amygdala is the affective component of pain. The CeA also induces prefrontal deactivation (by reciprocal modulation) and facilitation of nociception in the PAG (by the CeA–PAG output pathway). Activation of the basolateral amygdala produces GABAergic feed forward inhibition of the prefrontal cortex. Prefrontal deactivation reduces rewards. On the other hand, desynchronized ɣ band oscillation in the somatosensory cortex increases activity in the rACC, which increases aversion. This circuitry is mainly GABAergic and acts through cortical and subcortical areas. The sensory and affective component of pain is the main target of this pathway. The fine-tuning balance between descending facilitation and inhibition in this circuit is altered, and the descending pain modulation system (PAG and RMV) is inhibited. CeA = central amygdala, cLBP = chronic nonspecific low back pain, GABA = gamma aminobutyric acid, PAG = periaqueductal gray, rACC = rostral anterior cingulate cortex, RVM = rostral ventral medullary area

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 » Conclusions Top

This meta-analysis of fMRI studies identified statistically significant activation clusters in spontaneous pain and hyperalgesia–allodynia in cLBP patients. The clusters were in cortical and subcortical areas and were mostly superimposed on the GABAergic circuitry in chronic pain. No activation was found in the PAG–RMV–spinal cord axis. It is possible that, the imbalance in GABAergic circuitry leads to dysfunction of the descending pain modulation system, and this altered pain neuro-matrix is the key mechanism for the persisting pain in cLBP.


[Figure 3] and [Figure 4] were created with Dr. Akhil Dhanesh Goel, Department of Community and Family Medicine, All India Institute of Medical Sciences, Jodhpur and Dr. Nitesh Monohar Gonnade, Department of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, Jodhpur, India are thanked for their constant help and encouragement in this study.

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Conflicts of interest

There are no conflicts of interest.

 » References Top

Melzack R, Wall PD. Pain mechanisms: A new theory. Science 1965;150:971–9.  Back to cited text no. 1
Perl ER. Ideas about pain, a historical view. Nat Rev Neurosci 2007;8:71–80.  Back to cited text no. 2
van Tulder MW, Assendelft WJ, Koes BW, Bouter LM. Spinal radiographic findings and nonspecific low back pain. A systematic review of observational studies. Spine 1997;22:427–34.  Back to cited text no. 3
Chou R, Shekelle P. Will this patient develop persistent disabling low back pain? JAMA 2010;303:1295–302.  Back to cited text no. 4
Gilbert FJ, Grant AM, Gillan MGC, Vale LD, Campbell MK, Scott NW, et al. Low back pain: Influence of early MR imaging or CT on treatment and outcome--multicenter randomized trial. Radiology 2004;231:343–51.  Back to cited text no. 5
Oliveira CB, Maher CG, Pinto RZ, Traeger AC, Lin C-WC, Chenot J-F, et al. Clinical practice guidelines for the management of non-specific low back pain in primary care: An updated overview. Eur Spine J Off Publ Eur Spine Soc Eur Spinal Deform Soc Eur Sect Cerv Spine Res Soc 2018;27:2791–803.  Back to cited text no. 6
van Tulder M, Becker A, Bekkering T, Breen A, del Real MTG, Hutchinson A, et al. Chapter 3. European guidelines for the management of acute nonspecific low back pain in primary care. Eur Spine J Off Publ Eur Spine Soc Eur Spinal Deform Soc Eur Sect Cerv Spine Res Soc 2006;15(Suppl 2):S169-91.  Back to cited text no. 7
Kregel J, Meeus M, Malfliet A, Dolphens M, Danneels L, Nijs J, et al. Structural and functional brain abnormalities in chronic low back pain: A systematic review. Semin Arthritis Rheum 2015;45:229–37.  Back to cited text no. 8
Yuan C, Shi H, Pan P, Dai Z, Zhong J, Ma H, et al. Gray matter abnormalities associated with chronic back pain: A meta-analysis of voxel-based morphometric studies. Clin J Pain 2017;33:983–90.  Back to cited text no. 9
Ng SK, Urquhart DM, Fitzgerald PB, Cicuttini FM, Hussain SM, Fitzgibbon BM. The relationship between structural and functional brain changes and altered emotion and cognition in chronic low back pain brain changes: A systematic review of MRI and fMRI studies. Clin J Pain 2018;34:237–61.  Back to cited text no. 10
Greenwald JD, Shafritz KM. An integrative neuroscience framework for the treatment of chronic pain: From cellular alterations to behavior. Front Integr Neurosci 2018;12:18.  Back to cited text no. 11
Ji R-R, Kohno T, Moore KA, Woolf CJ. Central sensitization and LTP: Do pain and memory share similar mechanisms? Trends Neurosci 2003;26:696–705.  Back to cited text no. 12
Latremoliere A, Woolf CJ. Central sensitization: A generator of pain hypersensitivity by central neural plasticity. J Pain Off J Am Pain Soc 2009;10:895–926.  Back to cited text no. 13
Chou R, Qaseem A, Snow V, Casey D, Cross JT, Shekelle P, et al. Diagnosis and treatment of low back pain: A joint clinical practice guideline from the American College of Physicians and the American Pain Society. Ann Intern Med 2007;147:478–91.  Back to cited text no. 14
Kober H, Wager TD. Meta-analysis of neuroimaging data. Wiley Interdiscip Rev Cogn Sci 2010;1:293–300.  Back to cited text no. 15
Eickhoff SB, Bzdok D, Laird AR, Kurth F, Fox PT. Activation likelihood estimation meta-analysis revisited. Neuroimage 2012;59:2349–61.  Back to cited text no. 16
Baliki MN, Chialvo DR, Geha PY, Levy RM, Harden RN, Parrish TB, et al. Chronic pain and the emotional brain: Specific brain activity associated with spontaneous fluctuations of intensity of chronic back pain. J Neurosci 2006;26:12165–73.  Back to cited text no. 17
Foss JM, Apkarian AV, Chialvo DR. Dynamics of pain: Fractal dimension of temporal variability of spontaneous pain differentiates between pain States. J Neurophysiol 2006;95:730–6.  Back to cited text no. 18
Apkarian AV. Pain and brain changes. In: Practical Management of Pain. Philadelphia, PA: Elsevier/Mosby; 2014. p. 113–31.  Back to cited text no. 19
Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetc R, et al. Chapter 7: Systematic reviews of etiology and risk. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis. JBI; 2020. Available from:  Back to cited text no. 20
Wager TD, Lindquist M, Kaplan L. Meta-analysis of functional neuroimaging data: Current and future directions. Soc Cogn Affect Neurosci 2007;2:150–8.  Back to cited text no. 21
Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: A random-effects approach based on empirical estimates of spatial uncertainty. Hum Brain Mapp 2009;30:2907–26.  Back to cited text no. 22
Eickhoff SB, Nichols TE, Laird AR, Hoffstaedter F, Amunts K, Fox PT, et al. Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation. NeuroImage 2016;137:70–85.  Back to cited text no. 23
Müller VI, Cieslik EC, Laird AR, Fox PT, Radua J, Mataix-Cols D, et al. Ten simple rules for neuroimaging meta-analysis. Neurosci Biobehav Rev 2018;84:151–61.  Back to cited text no. 24
Tagliazucchi E, Balenzuela P, Fraiman D, Chialvo DR. Brain resting state is disrupted in chronic back pain patients. Neurosci Lett 2010;485:26–31.  Back to cited text no. 25
Buckalew N, Haut MW, Morrow L, Weiner D. Chronic pain is associated with brain volume loss in older adults: Preliminary evidence. Pain Med Malden Mass 2008;9:240–8.  Back to cited text no. 26
Tagliazucchi E, Balenzuela P, Fraiman D, Montoya P, Chialvo DR. Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state. Neurosci Lett 2011;488:158–63.  Back to cited text no. 27
Baliki MN, Petre B, Torbey S, Herrmann KM, Huang L, Schnitzer TJ, et al. Corticostriatal functional connectivity predicts transition to chronic back pain. Nat Neurosci 2012;15:1117–9.  Back to cited text no. 28
Hashmi JA, Baliki MN, Huang L, Baria AT, Torbey S, Hermann KM, et al. Shape shifting pain: Chronification of back pain shifts brain representation from nociceptive to emotional circuits. Brain 2013;136:2751–68.  Back to cited text no. 29
Chapter 3: Spinal Pain, Section 1: Spinal and Radicular Pain Syndromes in Descriptions of Chronic Pain Syndromes and Definitions of Pain Terms. IASP Press; 1994. Avaiable from : https://www.iasppain. org/publications/free-ebooks/classification-ofchronic- pain-second-edition-revised/.  Back to cited text no. 30
Kong J, Spaeth RB, Wey H-Y, Cheetham A, Cook AH, Jensen K, et al. S1 is associated with chronic low back pain: A functional and structural MRI study. Mol Pain 2013;9:43.  Back to cited text no. 31
Werneke MW, Hart DL. Categorizing patients with occupational low back pain by use of the Quebec Task Force Classification system versus pain pattern classification procedures: Discriminant and predictive validity. Phys Ther 2004;84:243–54.  Back to cited text no. 32
Mao C, Wei L, Zhang Q, Liao X, Yang X, Zhang M. Differences in brain structure in patients with distinct sites of chronic pain: A voxel-based morphometric analysis. Neural Regen Res 2013;8:2981–90.  Back to cited text no. 33
Rodriguez-Raecke R, Ihle K, Ritter C, Muhtz C, Otte C, May A. Neuronal differences between chronic low back pain and depression regarding long-term habituation to pain. Eur J Pain Lond Engl 2014;18:701–11.  Back to cited text no. 34
Ung H, Brown JE, Johnson KA, Younger J, Hush J, Mackey S. Multivariate classification of structural MRI data detects chronic low back pain. Cereb Cortex N Y N 1991 2014;24:1037–44.  Back to cited text no. 35
Baliki MN, Mansour AR, Baria AT, Apkarian AV. Functional reorganization of the default mode network across chronic pain conditions. PLoS One 2014;9:e106133.  Back to cited text no. 36
Tu Y, Jung M, Gollub RL, Napadow V, Gerber J, Ortiz A, et al. Abnormal medial prefrontal cortex functional connectivity and its association with clinical symptoms in chronic low back pain. Pain 2019;160:1308–18.  Back to cited text no. 37
Kim J, Mawla I, Kong J, Lee J, Gerber J, Ortiz A, et al. Somatotopically specific primary somatosensory connectivity to salience and default mode networks encodes clinical pain. Pain 2019;160:1594–605.  Back to cited text no. 38
Timmers I, de Jong JR, Goossens M, Verbunt JA, Smeets RJ, Kaas AL. Exposure in vivo induced changes in neural circuitry for pain-related fear: A longitudinal fMRI study in chronic low back pain. Front Neurosci 2019;13:970.  Back to cited text no. 39
Schneider I, Schmitgen MM, Boll S, Roth C, Nees F, Usai K, et al. Oxytocin modulates intrinsic neural activity in patients with chronic low back pain. Eur J Pain Lond Engl 2020;24:945–55.  Back to cited text no. 40
Tan W, Wang W, Yang Y, Chen Y, Kang Y, Huang Y, et al. Spinal manipulative therapy alters brain activity in patients with chronic low back pain: A longitudinal brain fMRI study. Front Integr Neurosci 2020;14:534595.  Back to cited text no. 41
Burton AK, Balagué F, Cardon G, Eriksen HR, Henrotin Y, Lahad A, et al. Chapter 2. European guidelines for prevention in low back pain : November 2004. Eur Spine J Off Publ Eur Spine Soc Eur Spinal Deform Soc Eur Sect Cerv Spine Res Soc 2006;15(Suppl 2):S136-68.  Back to cited text no. 42
Mayr A, Jahn P, Stankewitz A, Deak B, Winkler A, Witkovsky V, et al. Patients with chronic pain exhibit individually unique cortical signatures of pain encoding. Hum Brain Mapp 2022;43:1676–93.  Back to cited text no. 43
Derbyshire SWG, Jones AKP, Creed F, Starz T, Meltzer CC, Townsend DW, et al. Cerebral responses to noxious thermal stimulation in chronic low back pain patients and normal controls. NeuroImage 2002;16:158–68.  Back to cited text no. 44
Giesecke T, Gracely RH, Grant MAB, Nachemson A, Petzke F, Williams DA, et al. Evidence of augmented central pain processing in idiopathic chronic low back pain. Arthritis Rheum 2004;50:613–23.  Back to cited text no. 45
Deyo RA, Weinstein JN. Low back pain. N Engl J Med 2001;344:363–70.  Back to cited text no. 46
Lloyd D, Findlay G, Roberts N, Nurmikko T. Differences in low back pain behavior are reflected in the cerebral response to tactile stimulation of the lower back. Spine 2008;33:1372–7.  Back to cited text no. 47
Kobayashi Y, Kurata J, Sekiguchi M, Kokubun M, Akaishizawa T, Chiba Y, et al. Augmented cerebral activation by lumbar mechanical stimulus in chronic low back pain patients: An FMRI study. Spine 2009;34:2431–6.  Back to cited text no. 48
Matsuo Y, Kurata J, Sekiguchi M, Yoshida K, Nikaido T, Konno S-I. Attenuation of cortical activity triggering descending pain inhibition in chronic low back pain patients: A functional magnetic resonance imaging study. J Anesth 2017;31:523–30.  Back to cited text no. 49
Li T, Zhang S, Kurata J. Suppressed descending pain modulatory and enhanced sensorimotor networks in patients with chronic low back pain. J Anesth 2018;32:831–43.  Back to cited text no. 50
Yu S, Li W, Shen W, Edwards RR, Gollub RL, Wilson G, et al. Impaired mesocorticolimbic connectivity underlies increased pain sensitivity in chronic low back pain. NeuroImage 2020;218:116969.  Back to cited text no. 51
Borsook D, Sava S, Becerra L. The pain imaging revolution: Advancing pain into the 21st century. Neurosci Rev J Bringing Neurobiol Neurol Psychiatry 2010;16:171–85.  Back to cited text no. 52
Talbot K, Madden VJ, Jones SL, Moseley GL. The sensory and affective components of pain: Are they differentially modifiable dimensions or inseparable aspects of a unitary experience? A systematic review. Br J Anaesth 2019;123:e263–72.  Back to cited text no. 53
Kuner R, Kuner T. Cellular circuits in the brain and their modulation in acute and chronic pain. Physiol Rev 2021;101:213–58.  Back to cited text no. 54
Chang W-J, O'Connell NE, Beckenkamp PR, Alhassani G, Liston MB, Schabrun SM. Altered primary motor cortex structure, organization, and function in chronic pain: A systematic review and meta-analysis. J Pain 2018;19:341–59.  Back to cited text no. 55
Kuner R, Flor H. Structural plasticity and reorganisation in chronic pain. Nat Rev Neurosci 2016;18:20–30.  Back to cited text no. 56
Apkarian AV, Bushnell MC, Schweinhardt P. Representation of pain in the brain. In: Wall & Melzack's Textbook of Pain. Elsevier Health Sciences; 2013. p. 111–28.  Back to cited text no. 57
Godinho F, Magnin M, Frot M, Perchet C, Garcia-Larrea L. Emotional modulation of pain: Is it the sensation or what we recall? J Neurosci Off J Soc Neurosci 2006;26:11454–61.  Back to cited text no. 58
Misra G, Coombes SA. Neuroimaging evidence of motor control and pain processing in the human midcingulate cortex. Cereb Cortex N Y N 1991 2015;25:1906–19.  Back to cited text no. 59
Lima MC, Fregni F. Motor cortex stimulation for chronic pain: Systematic review and meta-analysis of the literature. Neurology 2008;70:2329–37.  Back to cited text no. 60
Coghill RC, Sang CN, Maisog JM, Iadarola MJ. Pain intensity processing within the human brain: A bilateral, distributed mechanism. J Neurophysiol 1999;82:1934–43.  Back to cited text no. 61
Strigo IA, Duncan GH, Boivin M, Bushnell MC. Differentiation of visceral and cutaneous pain in the human brain. J Neurophysiol 2003;89:3294–303.  Back to cited text no. 62
Lorenz J, Cross DJ, Minoshima S, Morrow TJ, Paulson PE, Casey KL. A unique representation of heat allodynia in the human brain. Neuron 2002;35:383–93.  Back to cited text no. 63
Apkarian AV, Sosa Y, Sonty S, Levy RM, Harden RN, Parrish TB, et al. Chronic back pain is associated with decreased prefrontal and thalamic gray matter density. J Neurosci Off J Soc Neurosci 2004;24:10410–5.  Back to cited text no. 64
Tölle TR, Kaufmann T, Siessmeier T, Lautenbacher S, Berthele A, Munz F, et al. Region-specific encoding of sensory and affective components of pain in the human brain: A positron emission tomography correlation analysis. Ann Neurol 1999;45:40–7.  Back to cited text no. 65
Zubieta JK, Smith YR, Bueller JA, Xu Y, Kilbourn MR, Jewett DM, et al. Regional mu opioid receptor regulation of sensory and affective dimensions of pain. Science 2001;293:311–5.  Back to cited text no. 66
Garcia-Larrea L, Peyron R. Pain matrices and neuropathic pain matrices: A review. Pain 2013;154(Suppl 1):S29–43.  Back to cited text no. 67
Tracey I, Mantyh PW. The cerebral signature for pain perception and its modulation. Neuron 2007;55:377–91.  Back to cited text no. 68
Lu C, Yang T, Zhao H, Zhang M, Meng F, Fu H, et al. Insular cortex is critical for the perception, modulation, and chronification of pain. Neurosci Bull 2016;32:191–201.  Back to cited text no. 69
Neugebauer V. Amygdala pain mechanisms. Handb Exp Pharmacol 2015;227:261–84.  Back to cited text no. 70
Borsook D, Upadhyay J, Chudler EH, Becerra L. A key role of the basal ganglia in pain and analgesia-insights gained through human functional imaging. Mol Pain 2010;6:27.  Back to cited text no. 71
Bingel U, Gläscher J, Weiller C, Büchel C. Somatotopic representation of nociceptive information in the putamen: An event-related fMRI study. Cereb Cortex N Y N 1991 2004;14:1340–5.  Back to cited text no. 72
Scott DJ, Heitzeg MM, Koeppe RA, Stohler CS, Zubieta J-K. Variations in the human pain stress experience mediated by ventral and dorsal basal ganglia dopamine activity. J Neurosci 2006;26:10789–95.  Back to cited text no. 73
Lane RD, Reiman EM, Bradley MM, Lang PJ, Ahern GL, Davidson RJ, et al. Neuroanatomical correlates of pleasant and unpleasant emotion. Neuropsychologia 1997;35:1437–44.  Back to cited text no. 74
Gallace A, Bellan V. The parietal cortex and pain perception: A body protection system. Handb Clin Neurol 2018;151:103–17.  Back to cited text no. 75
Alhajri N, Boudreau SA, Graven-Nielsen T. Angular gyrus connectivity at alpha and beta oscillations is reduced during tonic pain – Differential effect of eye state. NeuroImage Clin 2022;33:102907.  Back to cited text no. 76
Lamm C, Decety J. Is the extrastriate body area (EBA) sensitive to the perception of pain in others? Cereb Cortex N Y N 1991 2008;18:2369–73.  Back to cited text no. 77
Yu R, Gollub RL, Spaeth R, Napadow V, Wasan A, Kong J. Disrupted functional connectivity of the periaqueductal gray in chronic low back pain. NeuroImage Clin 2014;6:100–8.  Back to cited text no. 78
Heinricher MM. Pain modulation and the transition from acute to chronic pain. Adv Exp Med Biol 2016;904:105–15.  Back to cited text no. 79
Ji R-R, Berta T, Nedergaard M. Glia and pain: Is chronic pain a gliopathy? Pain 2013;154:S10–28.  Back to cited text no. 80
Ji R-R, Chamessian A, Zhang Y-Q. Pain regulation by non-neuronal cells and inflammation. Science 2016;354:572–7.  Back to cited text no. 81
Buckalew N, Haut MW, Aizenstein H, Rosano C, Edelman KD, Perera S, et al. White matter hyperintensity burden and disability in older adults: Is chronic pain a contributor? PM R 2013;5:471–80; quiz 480.  Back to cited text no. 82


  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1], [Table 2], [Table 3]


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