Deep brain stimulation of the ventral striatal area for poststroke pain syndrome: a magnetoencephalography study

Anatomo-physiological basis and applied techniques of electrical neuromodulation in chronic pain

Chronic pain, a complex and debilitating condition, poses a significant challenge to both patients and healthcare providers worldwide. Conventional pharmacological interventions often prove inadequate in delivering satisfactory relief while carrying the risks of addiction and adverse reactions. In recent years, electric neuromodulation emerged as a promising alternative in chronic pain management. This method entails the precise administration of electrical stimulation to specific nerves or regions within the central nervous system to regulate pain signals. Through mechanisms that include the alteration of neural activity and the release of endogenous pain-relieving substances, electric neuromodulation can effectively alleviate pain and improve patients' quality of life. Several modalities of electric neuromodulation, with a different grade of invasiveness, provide tailored strategies to tackle various forms and origins of chronic pain. Through an exploration of the anatomical and physiological pathways of chronic pain, encompassing neurotransmitter involvement, this narrative review offers insights into electrical therapies' mechanisms of action, clinical utility, and future perspectives in chronic pain management.

Contact Heat in Magnetoencephalography: A Systematic Review

BACKGROUND

Contact heat is commonly used in experimental research to evoke brain activity, most frequently acquired with electroencephalography (EEG). Although magnetoencephalography (MEG) improves spatial resolution, using some contact heat stimulators with MEG can present methodological challenges. This systematic review assesses studies that utilise contact heat in MEG, their findings and possible directions for further research.

METHODS

Eight electronic databases were searched for relevant studies, in addition to the selected papers' reference lists, citations and ConnectedPapers maps. Best practice recommendations for systematic reviews were followed. Papers met inclusion criteria if they used MEG to record brain activity in conjunction with contact heat, regardless of stimulator equipment or paradigm.

RESULTS

Of 646 search results, seven studies met the inclusion criteria. Studies demonstrated effective electromagnetic artefact removal from MEG data, the ability to elicit affective anticipation and differences in deep brain stimulation responders. We identify contact heat stimulus parameters that should be reported in publications to ensure comparisons between data outcomes are consistent.

CONCLUSIONS

Contact heat is a viable alternative to laser or electrical stimulation in experimental research, and methods exist to successfully mitigate any electromagnetic noise generated by PATHWAY CHEPS equipment - though there is a dearth of literature exploring the post-stimulus time window.

Graph theory analysis reveals an assortative pain network vulnerable to attacks

The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we employ graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Within this network, 63% of brain areas share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes that have similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain that processes nociceptive information. Furthermore, the vulnerability of this network to attack presents the possibility of alleviating pain by targeting the most connected brain areas in the network.

Brain stimulation for chronic pain management: a narrative review of analgesic mechanisms and clinical evidence

Chronic pain constitutes one of the most common chronic complaints that people experience. According to the International Association for the Study of Pain, chronic pain is defined as pain that persists or recurs longer than 3 months. Chronic pain has a significant impact on individuals' well-being and psychosocial health and the economy of healthcare systems as well. Despite the availability of numerous therapeutic modalities, treatment of chronic pain can be challenging. Only about 30% of individuals with non-cancer chronic pain achieve improvement from standard pharmacological treatment. Therefore, numerous therapeutic approaches were proposed as a potential treatment for chronic pain including non-opioid pharmacological agents, nerve blocks, acupuncture, cannabidiol, stem cells, exosomes, and neurostimulation techniques. Although some neurostimulation methods such as spinal cord stimulation were successfully introduced into clinical practice as a therapy for chronic pain, the current evidence for brain stimulation efficacy in the treatment of chronic pain remains unclear. Hence, this narrative literature review aimed to give an up-to-date overview of brain stimulation methods, including deep brain stimulation, motor cortex stimulation, transcranial direct current stimulation, repetitive transcranial magnetic stimulation, cranial electrotherapy stimulation, and reduced impedance non-invasive cortical electrostimulation as a potential treatment for chronic pain.

Graph theory analysis reveals an assortative pain network vulnerable to attacks

The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we use graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Sixty-three percent of brain areas in this structural pain network share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes with similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain processing nociceptive information, and the vulnerability of this network to attack opens up the possibility of alleviating pain by targeting the most connected brain areas in the network.

Deep Brain Stimulation, Stereotactic Radiosurgery and High-Intensity Focused Ultrasound Targeting the Limbic Pain Matrix: A Comprehensive Review

Chronic pain (CP) represents a socio-economic burden for affected patients along with therapeutic challenges for currently available therapies. When conventional therapies fail, modulation of the affective pain matrix using reversible deep brain stimulation (DBS) or targeted irreversible thalamotomy by stereotactic radiosurgery (SRS) and magnetic resonance (MR)-guided focused ultrasound (MRgFUS) appear to be considerable treatment options. We performed a literature search for clinical trials targeting the affective pain circuits (thalamus, anterior cingulate cortex [ACC], ventral striatum [VS]/internal capsule [IC]). PubMed, Ovid, MEDLINE and Scopus were searched (1990–2021) using the terms “chronic pain”, “deep brain stimulation”, “stereotactic radiosurgery”, “radioneuromodulation”, “MR-guided focused ultrasound”, “affective pain modulation”, “pain attention”. In patients with CP treated with DBS, SRS or MRgFUS the somatosensory thalamus and periventricular/periaquaeductal grey was the target of choice in most treated subjects, while affective pain transmission was targeted in a considerably lower number (DBS, SRS) consisting of the following nodi of the limbic pain matrix: the anterior cingulate cortex; centromedian-parafascicularis of the thalamus, pars posterior of the central lateral nucleus and internal capsule/ventral striatum. Although DBS, SRS and MRgFUS promoted a meaningful and sustained pain relief, an effective, evidence-based comparative analysis is biased by heterogeneity of the observation period varying between 3 months and 5 years with different stimulation patterns (monopolar/bipolar contact configuration; frequency 10–130 Hz; intensity 0.8–5 V; amplitude 90–330 μs), source and occurrence of lesioning (radiation versus ultrasound) and chronic pain ethology (poststroke pain, plexus injury, facial pain, phantom limb pain, back pain). The advancement of neurotherapeutics (MRgFUS) and novel DBS targets (ACC, IC/VS), along with established and effective stereotactic therapies (DBS–SRS), increases therapeutic options to impact CP by modulating affective, pain-attentional neural transmission. Differences in trial concept, outcome measures, targets and applied technique promote conflicting findings and limited evidence. Hence, we advocate to raise awareness of the potential therapeutic usefulness of each approach covering their advantages and disadvantages, including such parameters as invasiveness, risk–benefit ratio, reversibility and responsiveness.

Decoding pain from brain activity

Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.

Optogenetically-inspired neuromodulation: Translating basic discoveries into therapeutic strategies

Optogenetic tools allow for the selective activation, inhibition or modulation of genetically-defined neural circuits with incredible temporal precision. Over the past decade, application of these tools in preclinical models of psychiatric disease has advanced our understanding the neural circuit basis of maladaptive behaviors in these disorders. Despite their power as an investigational tool, optogenetics cannot yet be applied in the clinical for the treatment of neurological and psychiatric disorders. To date, deep brain stimulation (DBS) is the only clinical treatment that can be used to achieve circuit-specific neuromodulation in the context of psychiatric. Despite its increasing clinical indications, the mechanisms underlying the therapeutic effects of DBS for psychiatric disorders are poorly understood, which makes optimization difficult. We discuss the variety of optogenetic tools available for preclinical research, and how these tools have been leveraged to reverse-engineer the mechanisms underlying DBS for movement and compulsive disorders. We review studies that have used optogenetics to induce plasticity within defined basal ganglia circuits, to alter neural circuit function and evaluate the corresponding effects on motor and compulsive behaviors. While not immediately applicable to patient populations, the translational power of optogenetics is in inspiring novel DBS protocols by providing a rationale for targeting defined neural circuits to ameliorate specific behavioral symptoms, and by establishing optimal stimulation paradigms that could selectively compensate for pathological synaptic plasticity within these defined neural circuits.

A Deep Brain Stimulation Trial Period for Treating Chronic Pain

Early studies of deep brain stimulation (DBS) for various neurological disorders involved a temporary trial period where implanted electrodes were externalized, in which the electrical contacts exiting the patient's brain are connected to external stimulation equipment, so that stimulation efficacy could be determined before permanent implant. As the optimal brain target sites for various diseases (i.e., Parkinson's disease, essential tremor) became better established, such trial periods have fallen out of favor. However, deep brain stimulation trial periods are experiencing a modern resurgence for at least two reasons: (1) studies of newer indications such as depression or chronic pain aim to identify new targets and (2) a growing interest in adaptive DBS tools necessitates neurophysiological recordings, which are often done in the peri-surgical period. In this review, we consider the possible approaches, benefits, and risks of such inpatient trial periods with a specific focus on developing new DBS therapies for chronic pain.

Functional Magnetic Resonance Imaging Correlates of Ventral Striatal Deep Brain Stimulation for Poststroke Pain

OBJECTIVE

Deep brain stimulation (DBS) for pain has largely been implemented in an uncontrolled manner to target the somatosensory component of pain, with research leading to mixed results. We have previously shown that patients with poststroke pain syndrome who were treated with DBS targeting the ventral striatum/anterior limb of the internal capsule (VS/ALIC) demonstrated a significant improvement in measures related to the affective sphere of pain. In this study, we sought to determine how DBS targeting the VS/ALIC modifies brain activation in response to pain.

MATERIALS AND METHODS

Five patients with poststroke pain syndrome who were blinded to DBS status (ON/OFF) and six age- and sex-matched healthy controls underwent functional magnetic resonance imaging (fMRI) measuring blood oxygen level-dependent activation in a block design. In this design, each participant received heat stimuli to the affected or unaffected wrist area. Statistical comparisons were performed using fMRI z-maps.

RESULTS

In response to pain, patients in the DBS OFF state showed significant activation (p < 0.001) in the same regions as healthy controls (thalamus, insula, and operculum) and in additional regions (orbitofrontal and superior convexity cortical areas). DBS significantly reduced activation of these additional regions and introduced foci of significant inhibitory activation (p < 0.001) in the hippocampi when painful stimulation was applied to the affected side.

CONCLUSIONS

These findings suggest that DBS of the VS/ALIC modulates affective neural networks.

Quantitatively validating the efficacy of artifact suppression techniques to study the cortical consequences of deep brain stimulation with magnetoencephalography

Deep brain stimulation (DBS) is an established and effective treatment for several movement disorders and is being developed to treat a host of neuropsychiatric disorders including epilepsy, chronic pain, obsessive compulsive disorder, and depression. However, the neural mechanisms through which DBS produces therapeutic benefits, and in some cases unwanted side effects, in these disorders are only partially understood. Non-invasive neuroimaging techniques that can assess the neural effects of active stimulation are important for advancing our understanding of the neural basis of DBS therapy. Magnetoencephalography (MEG) is a safe, passive imaging modality with relatively high spatiotemporal resolution, which makes it a potentially powerful method for examining the cortical network effects of DBS. However, the degree to which magnetic artifacts produced by stimulation and the associated hardware can be suppressed from MEG data, and the comparability between signals measured during DBS-on and DBS-off conditions, have not been fully quantified. The present study used machine learning methods in conjunction with a visual perception task, which should be relatively unaffected by DBS, to quantify how well neural data can be salvaged from artifact contamination introduced by DBS and how comparable DBS-on and DBS-off data are after artifact removal. Machine learning also allowed us to determine whether the spatiotemporal pattern of neural activity recorded during stimulation are comparable to those recorded when stimulation is off. The spatiotemporal patterns of visually evoked neural fields could be accurately classified in all 8 patients with DBS implants during both DBS-on and DBS-off conditions and performed comparably across those two conditions. Further, the classification accuracy for classifiers trained on the spatiotemporal patterns evoked during DBS-on trials and applied to DBS-off trials, and vice versa, were similar to that of the classifiers trained and tested on either trial type, demonstrating the comparability of these patterns across conditions. Together, these results demonstrate the ability of MEG preprocessing techniques, like temporal signal space separation, to salvage neural data from recordings contaminated with DBS artifacts and validate MEG as a powerful tool to study the cortical consequences of DBS.