The dynamic pain connectome model was derived from brain imaging data in healthy subjects who had mind-wandering away from a painful stimulus. This work found that most brain regions were activated by noxious stimuli whether the mind wandered away from the stimulus or not. The responses of the salience and default mode networks and connectivity with antinociceptive areas showed mind wandering brain activity that included a clear distinction between trials in which subjects attended to pain vs. mind-wandered from pain . Increased functional connectivity between the medial prefrontal cortex–nucleus accumbens at the beginning of back pain predicts that patients will go on to develop chronic back pain; while patients with decreased connectivity in this circuit went on to recover from back pain . Structural brain imaging in subacute back pain patients was followed longitudinally for 3 years as they either recovered from or transitioned to chronic pain. Furthermore, these results indicate that persistence of chronic pain is predetermined by corticolimbic neuroanatomical factors .There is substantial overlap between the circuits involved in human placebo analgesia and those that mediate multiple forms of context-based modulation of pain behavior in rodents, including forebrain-brainstem pathways and opioid and cannabinoid systems in particular. Subcortical limbic volume asymmetry, sensorimotor cortical thickness, and functional coupling of prefrontal regions, anterior cingulate, and periaqueductal gray are predictive of placebo response .
One study found that placebo and nocebo effects are generated through differential engagement of the periaqueductal gray-rostral ventromedial medulla pathway,vertical grow rack system which likely influences pain experience by modulating activity at the dorsal horn level .Individuals have a set point around which different biological attributes can fluctuate transiently into different states. However,if one remains in a different state other than their set point for a considerable period , this different state is considered a new set point. In pain research it is important to consider trait and state pains to gain an understanding of not only an individual’s current pain state but also more broadly to their trait pain, which may be more reflective of their general condition . Resilience is a trait that is highly associated with chronic pain–related health outcomes. The neural correlates of both pain and trait resilience are critical to understand the brain– behavior relationship in chronic pain; yet, neural correlates of resilience in chronic pain states are unknown . Therefore, regional BOLD variability and circuit connectivity have potential to provide predictive power for pain resilience or vulnerability to chronic pain and treatment efficacy . Two reviews on mechanisms and imaging biomarkers for diabetic neuropathic pain review that diabetic peripheral neuropathy and associated pain have structural and functional central nervous system changes in the spinal cord, subcortex, and cortex . Diabetic peripheral neuropathy has been associated with changes in the thalamus. A decreased thalamic NAA/creatinine ratio is suggestive of thalamic neuronal dysfunction , and thalamic microvascular perfusion changes have also been observed . Smaller spinal cord crosssectional area has been observed in those with diabetic peripheral neuropathy . In fact, in diabetic peripheral neuropathy, diffusion tensor imaging techniques found posterior column damage in the cervical spinal cord .
Diabetic neuropathic pain is related to decreased NAA in the thalamus , increased thalamic vascularity , and spinal cord posterior column damage . Diabetic neuropathic pain is associated with increased regional brain gray matter volume loss localized to brain regions involved in somatosensory perception ; furthermore, in diabetic neuropathic pain, increased total gray matter atrophy is associated with impaired ability to walk . Diabetic neuropathic pain has been shown to be related to aberrant default mode functional connectivity , decreased functional connectivity between the thalamus and cortex , and decreased functional connectivity in attention networks . Altered fMRI activation responses to experimental heat pain in limbic and striatal brain circuits are related to the duration of diabetic neuropathic pain . Diabetic neuropathic pain is related to a double dissociation such that neuropathic pain intensity is more associated with thalamus-insular cortex functional connectivity and nerve deficits are more related to thalamus-somatosensory cortex functional connectivity . Diabetic neuropathic pain is also associated with decreased functional connectivity between the thalamus and amygdala , decreased gray matter volumes and decreased white matter connectivity in pain processing and pain modulation brain regions , decreased somatosensory cortical thickness related to cortical function dysfunction , increased activity in the anterior cingulate cortex , as well as ventrolateral periaqueductal gray functional connectivity is altered and correlates with magnitude of spontaneous pain and allodynic pain . Structural brain imaging has revealed changes to the brain associated with HIV peripheral neuropathy. Total cortical volume is smaller with HIV distal neuropathic pain . In fact, in HIV distal neuropathic pain the posterior cingulate cortex is the cortical region that was found to be smaller . In another sample of people living with HIV, subjective symptoms of HIV peripheral neuropathy were associated with smaller precuneus volumes which overlap with the posterior cingulate cortex .
Smaller brain volumes for HIV distal neuropathic pain are consistent with a general pattern that brain volumes are reduced for a variety of chronic pain conditions . Interestingly, the midbrain, thalamus and posterior cingulate cortex volumes are all reduced in HIV distal neuropathic paresthesia . It has been suggested that brain atrophy associated with HIV distal neuropathic paresthesia may precede brain atrophy associated with HIV distal neuropathic pain . More recent multi-modal brain imaging research has revealed structural brain changes associated with HIV peripheral neuropathy . HIV peripheral neuropathy is correlated with decreased white matter integrity running from the midbrain to the somatosensory cortex. HIV peripheral neuropathy severity is also associated with decreased generalized fractional anisotropy along the tracts of the external capsule in both hemispheres, appearing to lead along the lateral thalamus to sensorimotor cortex. A similar correlation is found in the superior bilateral cingulum. These results indicate ascending deafferentation in HIV peripheral neuropathy extends further downstream from damaged peripheral nerves than seen previously, into the cortex. HIV-associated distal neuropathic pain is associated with decreased fMRI resting state functional connectivity in the default mode network and increased functional connectivity in the salience network . Decreased connectivity between the medial prefrontal cortex and posterior cingulate cortex and stronger connectivity between the ACC and thalamus is associated with HIV distal neuropathic pain. In the setting of experimental heat pain, significant interaction has been found within the right anterior insula during expectation of experimental pain offset in that a group with HIV distal neuropathic pain compared group without HIV distal neuropathic pain exhibited increased insula activation in the feet compared to the hand . These findings are consistent with abnormal processing of expectation of experimental pain offset or abnormal pain relief mechanisms potentially due to increased negative expectation regarding the experience of chronic endogenous neuropathic pain. Anterior cingulate cerebral perfusion and gray matter density correlate with chemotherapy-induced peripheral neuropathy symptoms including pain . Patients with chemotherapyinduced peripheral neuropathy symptoms demonstrated greater activation during painful stimulation in the precuneus compared to healthy controls and exhibited hypo-activation of the right superior frontal gyrus compared to healthy controls. Painful stimuli delivered chemotherapyinduced peripheral neuropathy symptoms patients evoke differential activation of distinct cortical regions,cannabis grow equipment reflecting a unique pattern of central pain processing compared with healthy controls providing a tool for monitoring cerebral changes during anti-cancer and analgesic treatment . A population of mixed small-fiber peripheral neuropathy was used to investigate how dysfunction of skin nerves led to abnormal recruitment of pain-related brain regions, suggesting that the brain may be affected in SFN. Greater volume reduction in pain-processing regions, particularly the bilateral anterior cingulate cortices was associated with greater depletion of intraepidermal nerve fibers. There was significant reduction in functional connectivity from the anterior cingulate cortex to the insula pain-processing cortex that is linearly correlated with the severity of intraepidermal nerve fiber depletion . Similarly, another population of mixed small-fiber peripheral neuropathy the degree of skin nerve degeneration was associated with the reduction of connectivity between the thalamus and pain-related areas. Despite altered white matter connectivity, there was no change in white matter integrity assessed with fractional anisotropy. These findings indicate that alterations in structural connectivity may serve as a biomarker of maladaptive brain plasticity that contributes to neuropathic pain after peripheral nerve degeneration .
A population of Charcot-Marie-Tooth patients had abnormal diffusion tensor imaging findings indicative of significant cerebral white matter abnormalities. Diffusion tensor imaging abnormalities were correlated with clinical disability, suggesting that there is comorbidity of central nervous system damage with peripheral neuropathy in Charcot-Marie-Tooth patients . A population of patients with hereditary neuropathy with liability to pressure palsies were compared to a population of normal controls and the fractional anisotropy values of the patients were significantly lower in bilateral frontal, orbitofrontal, and temporal areas of white matter . Patient populations of paresthesia-dominant and pain-dominant patient groups were compared and contralesional cortical thickness were correlated with pain severity . Acquired and hereditary peripheral neuropathies are associated with increased functional connectivity of the left precuneus/posterior cingulate cortex in the default mode network. This increased connectivity in the default mode network is correlated with duration of peripheral neuropathy and severity of clinical total neuropathy score . As discussed in the introduction, if used in combination, biomarkers related to pain mechanisms offer the possibility to develop objective pain-related indicators that may help diagnosis, treatment, and understanding of pain pathophysiology . One possible application of such an approach might be to determine if a patient who is not communicative is experiencing pain. Another example may be to help guide selection of treatment for neuropathy, such as whether transcranial magnetic stimulation may alter network activity among those with neuropathy. Modeling pain brain mechanisms can be achieved using multi-modal brain imaging including functional magnetic resonance imaging, structural magnetic resonance imaging, diffusion tensor magnetic resonance imaging, electroencephalography, EMG, and PET . As we have reviewed here, in addition to using imaging biomarkers, composite pain biomarkers can be investigated using a multitude of non-imaging biomarkers. Multiple analytic approaches have been used to investigate composite pain biomarkers: composite algorithms have been investigated , unsupervised and supervised multivariate analyses have been used to distinguish pain groups and non-pain groups , supervised pattern recognition have been used to cluster diagnostic groups for different pain conditions , mechanism-based pharmacokinetic-pharmacodynamic modeling has been used to identify biomarkers that help diagnose pain and predict pain treatment , principal component analysis has been applied to biochemical markers to create distinct pain profiles , patterns of inflammatory blood cytokines and chemokines have been used to differentiate pain and non-pain groups , multi-variable data analysis using simultaneous analysis of 92 inflammation-related proteins with pain intensity and pain thresholds were used to identify protein patterns which distinguish pain and non-pain groups , metabolomics have been applied to chronic pain .As detailed above, chronic pain and neuropathic pain impact multiple organ systems. Advancing the value of pain biomarkers depends on selection of measurements and metrics that are the most mechanistically valid and informative, and combining the selected measurements such that they mechanistically and statistically maximize accurate classification. Advancement of measurement accuracy is vital and the subsequent steps of the approach are entirely contingent upon the success of this step. This literature for the domains discussed in this manuscript is too voluminous for a single review. In the above reviewed literature, we attempted principally to focus on which biological systems and which biomarkers should be the focus of measurement. For effective application of measurements of these domains it is important to discuss approaches for measurement selection. In Figure 2, we provide a significantly abbreviated schematic of key available statistical approaches to handling multi-modal datasets in building composite biomarkers. We have highlighted four general areas of statistics/machine learning: feature reduction , classification , regression , and clustering . Feature reduction can occur during or prior to classification, regression, or clustering. Feature reduction primarily focuses on two primary approaches: integration of measurements toward creation of a composite variable to simplify and enhance model performance, and effective feature reduction through variable selection to use optimal variables. Thus, feature reduction can represent the effective combining of strong measurements to a meaningful and robust latent variable or elimination of unnecessary, or statistically weak, measurements. Some methods, such as random forest, has built in feature reduction .