Pain Response and Associations with Psychiatric Symptoms Using an Affordable Electric Shock Task
Poster No:
2528
Submission Type:
Abstract Submission
Authors:
Benjamin Ely1, Tram Nguyen1, Zoe Baker1, Jasmin Richard1, Russell Tobe2, Vilma Gabbay3,2
Institutions:
1Albert Einstein College of Medicine, Bronx, NY, 2Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, 3University of Miami Miller School of Medicine, Miami, FL
First Author:
Co-Author(s):
Vilma Gabbay, M.D., M.S.
University of Miami Miller School of Medicine|Nathan S. Kline Institute for Psychiatric Research
Miami, FL|Orangeburg, NY
University of Miami Miller School of Medicine|Nathan S. Kline Institute for Psychiatric Research
Miami, FL|Orangeburg, NY
Introduction:
Pain is a key clinical feature of numerous physical and mental health conditions, including depression and substance use (Borsook 2016). However, stimulation methods commonly used to study pain (e.g. heat, pressure) require hardware that is expensive (>$50K US) and complex (e.g. pumped-water thermodes, pneumatic cuffs) to safely implement in an MR environment, leading fewer groups to study pain processing. Here, we describe a low-cost (<$10K US), solid-state electric pain task (ePain), modified from a previously published protocol (Ma 2016), and investigate pain response and associations with psychiatric symptoms in adolescents.
Methods:
Task: Subjects first completed an out-of-scanner calibration. An electrode was placed on the dorsal surface of the right foot and connected to an electric stimulator (Biopac, Goleta CA). Shocks (100Hz, 0.5ms pulses) were delivered for 3s starting at 10V and raised in 2.5-5V increments (max 100V) to determine when shocks could be painlessly felt (detection threshold), became painful (pain threshold), and were as painful as could be tolerated (pain limit). Immediately prior to fMRI, calibration was repeated in-scanner to confirm/adjust thresholds using an MR-compatible variant of the electric stimulator. ePain trials consisted of: cue (2s), ISI (4-12s), shock (1s linear ramp-up, 2s peak), pain rating (6s, 0-10 VAS), and ITI (4-12s). Runs included 5 trials with painful cues/shocks (pain limit voltage -10%) and 5 with non-painful cues/shocks (detection limit voltage +10%) in pseudo-random.
Data: Analyses were performed on a preliminary sample of 25 adolescents (age=15.4±2.5) with mood and anxiety symptoms enrolled in an actively recruiting study (see also our related abstract: Nguyen TNB et al. "Task fMRI Investigation of Pain Processing in Adolescent Cannabis Use"). Subjects filled out questionnaires to assess depression (BDI), anxiety (MASC), and anhedonia (TEPS) severity. Imaging was performed on a 3T Skyra scanner (Siemens, Germany) using protocols similar to Human Connectome Project (HCP) Lifespan studies (Harms 2018), including T1w anatomical MPRAGE (0.9mm isotropic) and three runs of ePain task EPI (2.3mm isotropic, TR=1s, 280 volumes, 5x multiband). Data processing included template normalization, realignment, and mild spatial smoothing (4mm FWHM). We modeled activation during painful vs. non-painful cues, painful vs. non-painful shocks, and pain ratings, as well as associations with clinical scales. Results were analyzed using FSL FEAT v6.00 (Woolrich 2004) with FLAME1+2 and outlier deweighting and evaluated at the cluster-corrected Z>2.58, two-tailed p<0.05 level.
Data: Analyses were performed on a preliminary sample of 25 adolescents (age=15.4±2.5) with mood and anxiety symptoms enrolled in an actively recruiting study (see also our related abstract: Nguyen TNB et al. "Task fMRI Investigation of Pain Processing in Adolescent Cannabis Use"). Subjects filled out questionnaires to assess depression (BDI), anxiety (MASC), and anhedonia (TEPS) severity. Imaging was performed on a 3T Skyra scanner (Siemens, Germany) using protocols similar to Human Connectome Project (HCP) Lifespan studies (Harms 2018), including T1w anatomical MPRAGE (0.9mm isotropic) and three runs of ePain task EPI (2.3mm isotropic, TR=1s, 280 volumes, 5x multiband). Data processing included template normalization, realignment, and mild spatial smoothing (4mm FWHM). We modeled activation during painful vs. non-painful cues, painful vs. non-painful shocks, and pain ratings, as well as associations with clinical scales. Results were analyzed using FSL FEAT v6.00 (Woolrich 2004) with FLAME1+2 and outlier deweighting and evaluated at the cluster-corrected Z>2.58, two-tailed p<0.05 level.
Results:
As shown in Fig. 1, painful vs. non-painful shocks elicited activation throughout canonical pain circuitry (Wager 2013), including the primary (SI) and secondary (SII) somatosensory cortices and thalamus contralateral to the electrode as well as the insula, anterior cingulate (ACC), and mid-cingulate (MCC). Painful vs. non-painful cues evoked stronger activation in the periaqueductal gray (PAG), important for descending pain modulation (Behbehani 1995), as well as visual areas. Pain ratings engaged regions involved in salience and threat monitoring (Legrain 2011), including the ACC, anterior insula, habenula, and dopaminergic midbrain.
Fig. 2 shows associations between task response and anticipatory anhedonia severity. Anhedonia correlated with activation in the bilateral insula, MCC, and posterior cingulate (PCC) during painful vs. non-painful cues; in the parahippocampus during painful vs. non-painful shocks; and in the pregenual and subgenual ACC during pain ratings.
Fig. 2 shows associations between task response and anticipatory anhedonia severity. Anhedonia correlated with activation in the bilateral insula, MCC, and posterior cingulate (PCC) during painful vs. non-painful cues; in the parahippocampus during painful vs. non-painful shocks; and in the pregenual and subgenual ACC during pain ratings.
·Figure 1. Response to painful vs. non-painful shocks, cues for painful vs. non-painful shocks, and pain ratings during the ePain task.
·Figure 2. Correlations between anticipatory anhedonia severity and ePain task activation in adolescents with mood and anxiety symptoms.
Conclusions:
The ePain task elicited a robust fMRI signal consistent with other noxious sensory stimuli, providing a straightforward way to probe pain circuitry without the need for highly specialized equipment. Moreover, we found extensive associations between neural pain responses and anhedonia severity, suggesting a link between reward dysfunction and aversion processing in youth.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Lifespan Development:
Early life, Adolescence, Aging
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Perception, Attention and Motor Behavior:
Perception: Pain and Visceral 1
Keywords:
Affective Disorders
Anxiety
Brainstem
FUNCTIONAL MRI
Pain
PEDIATRIC
Psychiatric
Psychiatric Disorders
Somatosensory
Thalamus
1|2Indicates the priority used for review
Provide references using author date format
Borsook D et al. (2016), ‘Reward deficiency and anti-reward in pain chronification’, Neurosci Biobehav Rev, vol. 68, pp. 282-297.
Harms MP et al. (2018), ‘Extending the Human Connectome Project across ages: imaging protocols for the Lifespan Development and Aging projects’, NeuroImage, vol. 183, pp. 972-984.
Legrain V et al. (2011), ‘The pain matrix reloaded: a salience detection system for the body’, Prog Neurobiol, vol. 93, pp. 111-124.
Ma Y et al. (2016), ‘Serotonin transporter polymorphism alters citalopram effects on human pain responses to physical pain’, NeuroImage, vol. 135, pp. 186-196.
Wager T et al. (2014), ‘An fMRI-based neurologic signature of physical pain’, N Engl J Med, vol. 368, pp. 1388-1397.
Woolrich MW et al., ‘Multilevel linear modelling for FMRI group analysis using Bayesian inference’, NeuroImage, vol. 21, pp. 1732-1747.