Neuroimaging Chronic Pain: What Have We Learned?
Neuroimaging Chronic Pain: What Have We Learned?
The use of neuroimaging technology to study chronic pain continues to gain interest and momentum. Noninvasive imaging techniques, including MRI, EEG, MEG and others, are being used with increasing frequency; this may largely be a function of the fact that neuroimaging can gather large amounts of data without requiring study subjects to engage in activity that could aggravate their pain – they are allowed to simply rest while the images are passively obtained.
In future, developments in three main areas hold the most promise to add to our understanding of CNS involvement in chronic pain, which will spur the subsequent development of novel therapies: combining imaging technologies to obtain simultaneous high-spatial and high-temporal resolution scans; identifying neurological signature patterns and prediction potential; and continuing to develop clinical neuroimaging-based interventions.
Researchers have begun to combine functional imaging technologies for use in some medical research, but few studies have used this technique to study chronic pain. For example, MRI can be combined with EEG or MEG, which achieves measures of both high spatial resolution from the MRI/fMRI scans and high temporal resolution from the EEG/MEG scans. The technology for simultaneous acquisition of MRI and EEG scans and for combining the images still needs to be developed. However, future studies using combined neuroimaging may provide invaluable insight into the brain changes in chronic pain states.
New advances in the technology for neuroimaging data analysis are gaining momentum and showing promise, specifically in the case of multivariate pattern analysis (MVPA) (for review:). MVPA is a machine-learning technology that can be applied as an algorithm to analyze large data sets and identify signature patterns that represent subgroups. Moreover, MVPA can function as a predictive tool; once a signature pattern has been identified in individuals with chronic pain versus healthy controls, data from a single individual can be classified as belonging to one of the groups, based on that individual's pattern of brain structure or activity and its similarity to the signature patterns of the group. MVPA technology has already been applied to identify acute pain related changes in healthy human volunteers, and has been extended to differentiate patients with chronic pain from healthy volunteers based on brain structure. Ultimately, it is anticipated that MVPA technology will advance neuroimaging to the next level, allowing it to be useful as a diagnostic tool to predict an individual's prognosis and define the appropriate therapies based on an individual's brain structure and activity patterns. In the future, this technology could also be combined with big data, such as phenotype and genetic information, to create a more personalized approach for diagnosing and treating the each patient. Longitudinal studies using MVPA may also provide scientific grounds for assessing the transition from acute to chronic pain. Overall, MVPA technology is a powerful tool that is expected to improve the clinical utility of neuroimaging for chronic pain and to advance neuroimaging analyses from the current standard of group comparisons to an individualized approach.
Neuroimaging continues to advance our understanding of how the CNS is affected by and involved in chronic pain, and neuroimaging interventions are being and gaining momentum as an alternative or supplement to pharmaceutical therapy, or both. Several studies of real-time neurofeedback for chronic pain have been conducted, but further research and additional clinical trials are still needed. The efficacy and benefits of real-time neurofeedback for an individual may be better harnessed in the future by the combining real-time neurofeedback fMRI and machine-learning classifiers (MVPA) to identify spatiotemporal brain maps ideal for individualized, real-time manipulation for each patient.
Although neurostimulation is invasive and is only implicated for use in the most severe, intractable cases of chronic pain, novel tools are being developed to better select patients who are most likely to benefit from this intervention. Implantation of neurostimulators is still an option for targeted manipulation of brain activity within specific brain regions, and there have been great advances in this technology since its inception. Current techniques use adaptive models and target brain regions, such as the motor cortex, that have the potential to activate multiple downstream effects.
Transcranial magnetic stimulation is also gaining popularity as an interventional and alternative method for reducing the symptoms of chronic pain (for review, see). Preliminary clinical trials of transcranial magnetic stimulation have demonstrated effective pain reduction that persists days to weeks after treatment. However, current investigations continue to search for ideal brain region targets and delivery specifications (such as parameters and treatment frequency).
Additional exciting advancements for the future use of neuroimaging in chronic pain-related therapy include the development of brain–computer interfaces using electrocorticography and visual feedback, which has been tested as a potential therapy for phantom limb pain. Advancements in the use of PET imaging are making it possible to use this technique to predict the efficacy of motor cortex stimulation, in particular using opioid binding and receptor density to predict the efficacy of motor cortex stimulation. Advances in present technology and combinations of old and new neuroimaging modalities will continue to help pain researchers decode the mysteries of the brain's response to chronic pain, which will enable the development of new and improved therapies for this complex and often disabling condition.
Greatest Future Potential for Neuroimaging in the Study of Chronic Pain
The use of neuroimaging technology to study chronic pain continues to gain interest and momentum. Noninvasive imaging techniques, including MRI, EEG, MEG and others, are being used with increasing frequency; this may largely be a function of the fact that neuroimaging can gather large amounts of data without requiring study subjects to engage in activity that could aggravate their pain – they are allowed to simply rest while the images are passively obtained.
In future, developments in three main areas hold the most promise to add to our understanding of CNS involvement in chronic pain, which will spur the subsequent development of novel therapies: combining imaging technologies to obtain simultaneous high-spatial and high-temporal resolution scans; identifying neurological signature patterns and prediction potential; and continuing to develop clinical neuroimaging-based interventions.
Good Qualities: Noninvasive & High-spatial & High-temporal Resolution
Researchers have begun to combine functional imaging technologies for use in some medical research, but few studies have used this technique to study chronic pain. For example, MRI can be combined with EEG or MEG, which achieves measures of both high spatial resolution from the MRI/fMRI scans and high temporal resolution from the EEG/MEG scans. The technology for simultaneous acquisition of MRI and EEG scans and for combining the images still needs to be developed. However, future studies using combined neuroimaging may provide invaluable insight into the brain changes in chronic pain states.
Multivariate Pattern Analysis: Machine Learning Technology
New advances in the technology for neuroimaging data analysis are gaining momentum and showing promise, specifically in the case of multivariate pattern analysis (MVPA) (for review:). MVPA is a machine-learning technology that can be applied as an algorithm to analyze large data sets and identify signature patterns that represent subgroups. Moreover, MVPA can function as a predictive tool; once a signature pattern has been identified in individuals with chronic pain versus healthy controls, data from a single individual can be classified as belonging to one of the groups, based on that individual's pattern of brain structure or activity and its similarity to the signature patterns of the group. MVPA technology has already been applied to identify acute pain related changes in healthy human volunteers, and has been extended to differentiate patients with chronic pain from healthy volunteers based on brain structure. Ultimately, it is anticipated that MVPA technology will advance neuroimaging to the next level, allowing it to be useful as a diagnostic tool to predict an individual's prognosis and define the appropriate therapies based on an individual's brain structure and activity patterns. In the future, this technology could also be combined with big data, such as phenotype and genetic information, to create a more personalized approach for diagnosing and treating the each patient. Longitudinal studies using MVPA may also provide scientific grounds for assessing the transition from acute to chronic pain. Overall, MVPA technology is a powerful tool that is expected to improve the clinical utility of neuroimaging for chronic pain and to advance neuroimaging analyses from the current standard of group comparisons to an individualized approach.
Brain-based Therapies: Real-time fMRI Neurofeedback & Neurostimulation
Neuroimaging continues to advance our understanding of how the CNS is affected by and involved in chronic pain, and neuroimaging interventions are being and gaining momentum as an alternative or supplement to pharmaceutical therapy, or both. Several studies of real-time neurofeedback for chronic pain have been conducted, but further research and additional clinical trials are still needed. The efficacy and benefits of real-time neurofeedback for an individual may be better harnessed in the future by the combining real-time neurofeedback fMRI and machine-learning classifiers (MVPA) to identify spatiotemporal brain maps ideal for individualized, real-time manipulation for each patient.
Although neurostimulation is invasive and is only implicated for use in the most severe, intractable cases of chronic pain, novel tools are being developed to better select patients who are most likely to benefit from this intervention. Implantation of neurostimulators is still an option for targeted manipulation of brain activity within specific brain regions, and there have been great advances in this technology since its inception. Current techniques use adaptive models and target brain regions, such as the motor cortex, that have the potential to activate multiple downstream effects.
Transcranial magnetic stimulation is also gaining popularity as an interventional and alternative method for reducing the symptoms of chronic pain (for review, see). Preliminary clinical trials of transcranial magnetic stimulation have demonstrated effective pain reduction that persists days to weeks after treatment. However, current investigations continue to search for ideal brain region targets and delivery specifications (such as parameters and treatment frequency).
Additional exciting advancements for the future use of neuroimaging in chronic pain-related therapy include the development of brain–computer interfaces using electrocorticography and visual feedback, which has been tested as a potential therapy for phantom limb pain. Advancements in the use of PET imaging are making it possible to use this technique to predict the efficacy of motor cortex stimulation, in particular using opioid binding and receptor density to predict the efficacy of motor cortex stimulation. Advances in present technology and combinations of old and new neuroimaging modalities will continue to help pain researchers decode the mysteries of the brain's response to chronic pain, which will enable the development of new and improved therapies for this complex and often disabling condition.
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