UTCSP Translational Seminar Series with Dr. Tasha Stanton
Dr. Tasha Stanton, Associate Professor in Clinical Pain Neuroscience, from University of South Australia, speaks about ddvances in exercise-based virtual and mediated reality for chronic pain.
On Thursday May 4th 2023 from 4-6 pm, don’t miss the chance to hear from Dr. Tasha Stanton (Associate Professor in Clinical Pain Neuroscience, from University of South Australia). Dr. Stanton’s talk: Advances in exercise-based virtual and mediated reality for chronic pain. After the 45-minute talk, there will be a 15-minute Q&A, followed by light refreshments.
Hosted by the UTCSP Knowledge Translation Committee Trainee Members, this first talk in a series of translational seminars is an opportunity to hear from speakers addressing various topics related to pain-science and pain-research.
Speaker:
Dr. Tasha Stanton
Associate Professor in Clinical Pain Neuroscience, University of South Australia
Date:
Thursday, May 4th 2023
Location:
Health Sciences Building
Room 610
The impact of cannabis industry sponsorship on the research process
The “funding effect” is an empirically biased effect in which industry-funded research is more likely to produce favourable results and conclusions that benefit industry sponsors. The underlying mechanisms that encourage the funding effect may include publication bias and selective methodology/experimental design. Scientists may adjust their research agendas based on the needs of the industry, given that these industry partnerships offer a wealth of funding. These industry sponsorships may also lead to financial conflict of interests. Despite the positive impact industry sponsorship and corporate research have had on research and innovation, there is still concern surrounding the implications of this relationship on research integrity and policy development.
Written by:
Vaidhehi Veena Sanmugananthan
Edited by:
Georgia Hadjis
Leading up to Canada’s legalization of cannabis, cannabis companies partnered with academics to research the understanding, medical potential, and creation of cannabis compounds to make research claims for the cannabis market. UTCSP scientist Dr. Daniel Buchman, UofT Lawrence S. Bloomberg Faculty of Nursing co-PI Dr. Quinn Grundy, and colleagues conducted a meta-research study to 1) identify research with statements of disclosure addressing funding or financial relationships with Canadian cannabis companies, 2) describe the research being conducted with cannabis companies and sponsorship towards the studies, and 3) identify the demographics of the participants included.
From May – August 2021 the authors identified sampling licensed, prominent Canadian cannabis companies, their subsidiaries, and searching each company in the PubMed conflict of interest statement search interface. The authors included 156 articles with disclosures of support or conflicts of interest with Canadian cannabis companies. Overall, the authors found evidence of the involvement of Canadian cannabis companies in the conduct and sponsorship of research. Over half of the articles were not primarily cannabis-focused but listed a cannabis company in the disclosure statement. Of the cannabis-focused articles, topics ranged from: cannabis as a treatment of medical conditions (21%); mediating harm and substance use reduction (14%); product safety (14%); and preclinical animal studies (8%). Demographics were generally under-reported in human empirical studies, most of which were adults (90%) and predominantly white (82%) and male (59%).
The authors suggest that cannabis companies in Canada conduct similar research to other industries (pharmaceuticals, tobacco, alcohol, food) by:
- sponsoring research that aids product development and testing;
- expanding use indication, and;
- financially supporting key-opinion leaders.
The largest proportion of studies focused on the use of cannabis for different conditions (e.g., chronic pain) and harm reduction, indicating that discussions are occurring in this industry surrounding harm reduction through research-related partnerships. The under-reporting and lack of diversity in demographics warrants further research to incorporate an intersectional lens and involve other communities to enhance equity-oriented research in this field. It is suggested that policy makers keep a close eye on and prioritize independence and community engagement in the research process to ensure high quality, independent cannabis research.
Neurons in the somatosensory cortex encode multiple features of vibrotactile signals using firing frequency
Vibrotactile sensation is processed in the somatosensory cortex in the brain. Neurons can encode different properties of stimuli, including the frequency or magnitude of a sound or vibrotactile sensation, in their firing frequency. Pyramidal neurons in the primary sensory cortex (S1), however, have a limited firing rate and cannot match the frequency of fast vibrotactile inputs one-to-one. To assess how pyramidal neurons encode features of vibrotactile sensations delivered by primary afferents, UTCSP member Dr. Steven Prescott and colleagues created computational simulations and performed electrophysiological recordings on slices of these neurons in S1 to explore individual and population-level coding.
Written by:
Quinn Pauli
Edited by:
Georgia Hadjis
Leading up to Canada’s legalization of cannabis, cannabis companies partnered with academics to research the understanding, medical potential, and creation of cannabis compounds to make research claims for the cannabis market. UTCSP scientist Dr. Daniel Buchman, UofT Lawrence S. Bloomberg Faculty of Nursing co-PI Dr. Quinn Grundy, and colleagues conducted a meta-research study to 1) identify research with statements of disclosure addressing funding or financial relationships with Canadian cannabis companies, 2) describe the research being conducted with cannabis companies and sponsorship towards the studies, and 3) identify the demographics of the participants included.
An adaptive exponential integrate-and-fire computational model was first adjusted to match the properties of S1 pyramidal neurons. To simulate physiological electrical “noise” due to random opening and closing of ion channels or background synaptic input, the model was tested in the presence or absence of uncorrelated noise. Using this model, the authors found that without the inclusion of noise, higher frequency inputs were coded in a nearly identical pattern as lower frequency inputs due to limitations in neuronal firing rates. However, when noise approximating in vivo conditions was included in the model, cycles of stimulation were skipped irregularly, resulting in spiking intervals that correlated with the frequency of the input. By delivering and recording electrical signals from neurons directly, the authors also found that S1 neurons fire intermittently during high frequency inputs, which allowed population responses to be modulated in sync with input frequency.
The authors found that without the inclusion of noise, higher frequency inputs were coded in a nearly identical pattern as lower frequency inputs due to limitations in neuronal firing rates. However, when noise approximating in vivo conditions was included in the model, cycles of stimulation were skipped irregularly, resulting in spiking intervals that correlated with the frequency of the input.
Together, the simulations and electrophysiological recordings indicate that S1 neurons can encode information about the frequency and amplitude of periodic vibrotactile inputs using firing intervals and rate, respectively. Crucially, this coding is aided by physiological noise, which modulates the reliability of firing without disrupting its precision. Overall, this study provides novel insights on how neurons encode multiple stimulus features in their firing rates.
UTCSP Scientific Meeting 2023
Join us for our 2023 UTCSP Scientific Meeting at Hart House
9:00 am – Registration
9:30 am – Opening Remarks from Rachael and Rob
9:45 am – Dr. Ruth Ross
10:30 am – Coffee Break and Poster Session
11:00 am – UTCSP Pain Scientist Trainee Awards
11:15 am – Dr. Robyn J. Crook
12:15 pm – Lunch
1:00 pm – Massieh Moayedi
1:45 pm – Dr. Vitaly Napadow
2:45 pm – Coffee Break and Poster Session
3:15 pm – Poster awards and Closing Remarks
Date:
Monday, March 20th, 2023
Location:
Music Room, Hart House, University of Toronto
In vivo construction of the central amygdala and trigeminal motor nucleus white matter pathway in humans using 7T and 3T Diffusion Weighted Imaging
The mandibular branch of the trigeminal nerve controls muscles of mastication through an area of the pontine brainstem called the trigeminal motor nucleus (5M). In rodents, 5M motor neurons have been shown to be involved in various circuits that contribute to jaw functions. Mapping these circuits in humans has been challenging because of the complex structure of the brainstem, the prevalence of crossing fibers, and the need for high-resolution technology to unravel these circuits.
Written by:
Vaidhehi Veena Sanmugananthan
Edited by:
Georgia Hadjis
UTCSP scientists Dr. Massieh Moayedi and Dr. Iacopo Cioffi and their team used Human Connectome Project (HCP) data to probe white matter pathways in the human brainstem to see if they could resolve a similar circuit in humans. Their main focus was on the pathway between 5M and the central nucleus of the amygdala (CeA), as the CeA has connections to autonomic centers in the brainstem and cortex, and plays an important role in modulating responses (physiological and behavioural) to various stimuli. To resolve this pathway, they performed probabilistic tractography on both 7T and 3T diffusion weighted imaging (DWI) scans. As a negative control, the authors performed a tractography analysis between 5M and the basolateral amygdala (BLAT), an amygdalar subregion that projects to the cortex but not to the brainstem. The connectivity strength between the BLAT-5M and CeA-5M circuits was then compared, expecting much lower connectivity strength in the BLAT-5M circuit than the CeA-5M circuit. The analysis was performed on 30 healthy individuals (17 females, mean age ± SD: 30.6 ± 2.6 years; 13 males, mean age ± SD: 27.5 ± 3.0 years).
An adaptive exponential integrate-and-fire computational model was first adjusted to match the properties of S1 pyramidal neurons. To simulate physiological electrical “noise” due to random opening and closing of ion channels or background synaptic input, the model was tested in the presence or absence of uncorrelated noise. Using this model, the authors found that without the inclusion of noise, higher frequency inputs were coded in a nearly identical pattern as lower frequency inputs due to limitations in neuronal firing rates. However, when noise approximating in vivo conditions was included in the model, cycles of stimulation were skipped irregularly, resulting in spiking intervals that correlated with the frequency of the input. By delivering and recording electrical signals from neurons directly, the authors also found that S1 neurons fire intermittently during high frequency inputs, which allowed population responses to be modulated in sync with input frequency.
Within each hemisphere, the authors found significantly greater connectivity strength in the CeA–5M circuit compared to the BLAT–5M circuit in both 7T and 3T scans. The authors conclude that their findings are the first to demonstrate an in vivo construction of the CeA-5M pathway in humans. This should encourage future studies to explore the role of this circuit in humans in more detail.
Impairments in the temporal organization of neural activity in patients with neuropathic pain
Treating neuropathic pain, often characterized by intense shooting, stabbing, or burning pain, is a major healthcare challenge. It is thought that the dynamic organization of neural networks is affected in patients with chronic pain.
Written by:
Quinn Pauli
Edited by:
Georgia Hadjis
Electrical activity may be used as a potential diagnostic and therapeutic to target these affected networks, referred to as ‘brain microstates’. Electrical activity can be measured using magnetoencephalography (MEG), a sensitive imaging tool with high temporal resolution that can identify neuronal oscillations in the alpha frequency band, which is thought to be a potential neuromarker of abnormal brain activity in chronic pain.In the present study, UTCSP members Dr. Camille Fauchon and Dr. Andrew Kim, along with Dr. Karen Davis, Dr. Anuj Bhatia and colleagues applied a Hidden Markov Model (HMM) statistical approach. to MEG data to divide cortical activity on the scalp surface into organized brain states to identify activity changes that accompany neuropathic pain.
Abnormalities in neural oscillations can be an indication of pathology, and by uniquely combining an HMM approach to MEG, the authors were able to estimate when certain brain networks are active, and thus determine the impact of pain on neural activity. Forty patients with chronic neuropathic pain were compared to 40 age- and sex-matched healthy controls, where twelve cognitive brain states in the dynamic pain connectome were identified for each patient at rest. Distinct abnormalities in temporal neural activity were observed in the neuropathic pain group, indicating impaired coordination and engagement of network activity over time. Specifically, patients spent more time in the sensorimotor state and less time in the dorsal attention state compared to healthy controls. Patients also demonstrated greater alpha power in the state representing the ascending nociceptive pathway. Together, these results suggest abnormal pain regulation in patients with neuropathic pain.
The main findings this study, published in Communications Biology, point to a potential impairment in neuronal communication between brain areas involved in nociceptive processing and pain suppression in people living with chronic neuropathic pain. This study showed for the first time that the HMM approach applied to MEG data can capture brain microstates underlying pain, while identifying correlational brain state markers for neuropathic pain conditions. Ultimately, exploring the dynamics of brain activity in chronic pain populations may lead to novel diagnostic and therapeutic approaches in the clinic.
Brain-age gaps as a biomarker for pain treatment in certain pain conditions
Chronic pain can impact many different parts of the body, leading to serious structural and functional effects on the nervous system. Chronic pain and its burden on the brain becomes more prevalent in older adults. In addition, grey and white matter reorganization in the brain is thought to be accelerated in those with chronic pain.
Written by:
Vaidhehi Veena Sanmugananthan
Edited by:
Georgia Hadjis
Gaussian process regression is an adaptive supervised multivariate statistical learning method found to be predictive of chronical age based on neuroimaging data. Models using this method have been used to determine brain predicted age differences across different diseases. Many of these studies have revealed that accelerated brain aging differences in these conditions vs. healthy controls are associated with increased risk of mortality and reduced cognitive abilities.
Studies have shown that a mixed cohort of people with chronic pain have larger brain-age gaps (brain-AGE), suggesting accelerated aging of the brain in chronic pain. Since chronic pain can differ in etiology, severity, pain frequency and sex-linked prevalence, brain-AGE could potentially differ across chronic pain disorders. In this study, UTCSP scientist Dr. Mojgan Hodaie and colleagues aimed to determine brain-AGE across 3 different chronic pain conditions: trigeminal neuralgia [TN], low back pain [BP] and osteoarthritis [OA]. They also examined sex effects in brain-AGE in these pain disorders and whether they relate to TN pain treatment.
A machine learning model was built using publically available neuroimaging data from a 3 Tesla MRI scanner of 812 healthy controls (HCs) in order to extract brain-AGE from 45 TN, 52 OA and 50 BP participants. They used false discovery rate Welch t-tests to delineate significant differences in brain-AGE between each pain group with HCs. The authors found that TN and OA cohorts have significantly larger brain-AGE, and across all pain groups, brain-AGE was driven by females. Large brain-AGE in the TN group was associated with gamma knife radiosurgery for pain and inversely associated with age of diagnosis of the pain disorder. These results suggest that younger women with TN are a potential vulnerable group that may require more prevalent and earlier chronic pain interventions. Based on these results, the authors concluded that brain-AGE could likely be used as a biomarker for efficacy of pain treatment.
These results suggest that younger women with TN are a potential vulnerable group that may require more prevalent and earlier chronic pain interventions. Based on these results, the authors concluded that brain-AGE could likely be used as a biomarker for efficacy of pain treatment.
Project ECHO Ontario Chronic Pain and Opioid Stewardship: minimizing distances and amplifying evidence-based care
The Project extension for community healthcare outcomes (Project ECHO) is a bidirectional teaching strategy that aims to disseminate knowledge and to increase the implementation of best practice in primary care in remote areas. Ontario has implemented the project since 2014 as the ECHO Ontario Chronic Pain and Opioid Stewardship. This unique educational model offers training via videoconferencing for primary care providers (PCP) to deliver specialised care for chronic pain disorders.
The four pillars of ECHO are (1) videoconferencing, (2) sharing best practices, (3) case-based learning, and (4) continuous outcome monitoring. Program ECHO uses videoconference technology to leverage scarce healthcare resources, minimizing distances between specialty and primary care and enabling clinicians from remote areas to discuss their patients’ cases with an expert interprofessional team. In addition, specialist mentors from the ECHO clinic share best practices with PCP to reduce variation in care and improve patient outcomes. PCP participants frequently evolve to become centers of excellence and start providing specialist care to their geographical area as they learn through interaction with the ECHO clinic. The case-based learning process occurs while the ECHO clinic prepares and shares a summary of the discussed case, summary of all recommendations, and relevant community resources not only with the person who presented the case but also with all the participants, which generalises the learning.
Benefits of participating in ECHO include reduction in patients’ expenses and time spent traveling to urban centers, improvement in quality of care in rural and remote areas, provision of no-cost continuing medical education, opportunities for professional interaction with colleagues, and access to specialists. As the ECHO model evolves, better methods for monitoring outcomes and increased rural access to primary care for ECHO will enhance the reach and efficacy of this program.
Benefits of participating in ECHO include reduction in patients’ expenses and time spent traveling to urban centers, improvement in quality of care in rural and remote areas, provision of no-cost continuing medical education, opportunities for professional interaction with colleagues, and access to specialists. As the ECHO model evolves, better methods for monitoring outcomes and increased rural access to primary care for ECHO will enhance the reach and efficacy of this program.
UTCSP members showcase their pain research at Neuroscience 2019
From October 19 to 23 2019, the Society for Neuroscience hosted their 49th Annual Meeting in Chicago, IL. It was undoubtedly an important and lively event with 27,832 attendees from 75 countries, 13,677 abstract presentations, and 833 sessions.
This platform provided the opportunity for UTCSP scientists and trainees to showcase their exciting research through poster and oral presentations. The three main nanosymposiums on pain this year were: 1) Pain and Itch Behavior, Circuitry, and Novel Techniques, 2) Pain Imaging and Perception, and 3) New Approaches for Pain Assessment and Treatment. UTCSP trainees were among the speakers of these sessions.
UTCSP trainee and postdoctoral fellow Kathy Halievski (Salter lab) gave a talk at the “Pain and Itch Behavior, Circuitry, and Novel Techniques” nanosymposium on exploring the curious interplay between pain, sex, and the gut microbiota. Specifically, the project aim was to determine whether the microbiota affects acute nociception or hypersensitivity following pain injury in an animal model, and potential sex differences between this interaction. While sex differences were present in certain pain assays, overall, antibiotic-mediated gut microbiota depletion did not affect behavioural responses in acute nociception or in injury-induced hypersensitivity. These findings are important to consider as this field develops, since some, but not all types of pain exhibit an interplay with the gut microbiota.
At the “Pain Imaging and Perception” nanosymposium, UTCSP trainees and graduate students Peter Hung and Sarasa Tohyama (Hodaie lab) gave a talk on advanced structural imaging in patients with trigeminal neuralgia. Hung and colleagues created a machine learning model to predict long-term response to radiosurgical treatment for trigeminal neuralgia from pre-treatment cortical thickness measurements. After cross-validation, the machine learning model predicted long-term response with an 76.5% accuracy. Thus, this brain-based model surpassed prior white matter nerve-level models for this clinical population. These findings hold strong clinical translational potential, as they may help guide treatment decision-making in the clinic.
Tohyama and colleagues identified and defined a new subtype of trigeminal neuralgia, which they termed “lesional trigeminal neuralgia”, using a clinical and neuroimaging approach. They found that patients with this condition are clinically indistinguishable from classical trigeminal neuralgia, but have a single, isolated, plaque-like lesion in the brainstem. Importantly, these patients are refractory to surgical treatment. Thus, the study highlights the important clinical implications of distinguishing this group from other known types of trigeminal neuralgia in the clinic. The study also points to the role of novel treatment strategies, such as neuromodulation, for their management.
Overall, Neuroscience 2019 showcased the great breadth and quality of neuroscience research, and in particular, pain research at the international level. UTCSP members were an integral part of this meeting, which fostered exciting connections, collaborations, and valuable ideas.
Children with anxiety recall more pain 1 year post-surgery
When recalling post-operative pain, patients who have a negatively-biased recall of pain (i.e. recalled pain is worse than originally reported pain) are at greater risk for the development of chronic pain. In this study by UTCSP scientists, Joel Katz, Jennifer Stinson, and colleagues sought to identify risk factors that promote negatively-biased recall of post-surgical pain in children.
In this prospective study, the authors followed patients at several timepoints throughout their first year post-surgery and asked whether anxiety pre- and post-surgery in children was correlated with negatively-biased recall of their post-operative pain. Specifically, they assessed 3 forms of anxiety: pain anxiety (anxiety during, or in anticipation of, pain), anxiety sensitivity (sensitivity to the symptoms experienced with anxiety), and pain catastrophizing (helplessness or magnification of pain) and three aspects of their current and recalled pain intensity: pain at rest, pain during movement, and pain unpleasantness.
They found that a patient’s anxiety sensitivity and pain catastrophizing pre-surgery were predictive of negatively-biased pain memory at both 6 and 12 months post-surgery. However, the greatest predictor of negatively-biased memory of pain was catastrophizing pain immediately post-surgery (48-72 hours after), specifically, movement-evoked pain. Notably, this study is the first post-surgery pediatric pain memory study that distinguishes between pain at rest and movement-evoked pain. The authors also found that patients with greater negatively-biased recall of pain reported higher pain intensity at 6 and 12 months post-surgery, suggesting a possibility that current pain intensity can bias pain recall.
In summary, the results of this study identify pain catastrophizing during the acute recovery period (i.e., 48 and 72 hours after surgery) as a significant predictor of negatively-biased pain recall long-term (6 and 12 months after surgery). Thus, implementing therapies to reduce pain catastrophizing in patients during this critical period is important to reduce negatively-biased memory of pain, and in turn, hopefully reduce the risk of chronic pain development.
They found that a patient’s anxiety sensitivity and pain catastrophizing pre-surgery were predictive of negatively-biased pain memory at both 6 and 12 months post-surgery. However, the greatest predictor of negatively-biased memory of pain was catastrophizing pain immediately post-surgery (48-72 hours after), specifically, movement-evoked pain.