Adverse childhood experiences, brain efficiency, and the development of pain symptoms in youth

Abstract

Background: Adverse childhood experiences (ACEs) are often reported by youths with chronic pain, and both ACEs and chronic pain disrupt how information is processed. However, it is unknown whether changes to shared neural networks underlie the relationship between ACEs and the development of pain symptoms. This study explored the relationships between ACEs, brain efficiency, and pain symptomology in youth.

Methods: A community sample of youths aged 14-18 years underwent MRIs, answered trauma and pain questionnaires, and underwent pain sensory testing, twice, 3 months apart (N_baseline = 44; N_follow-up = 42). Sensory testing determined thresholds for mechanical and thermal stimuli. Global and local network efficiencies were evaluated using graph theory. Generalized estimating equations were applied to examine whether brain efficiency moderated the relationships between ACEs, pain intensity, and pain sensitivity (i.e., mechanical detection, heat pain, and temperature change thresholds).

Results: There was a significant interaction between ACEs and global brain efficiency in association with pain intensity (β = -0.31, p = 0.02) and heat pain (β = -0.29, p = 0.004). Lower global brain efficiency exacerbated the relationship between ACEs and pain intensity (θ_{X → Y|W = -1.16} = 0.37, p = 0.05), and heat pain sensitivity (θ_{X → Y|W = -1.30} = 0.44, p = 0.05). Higher global brain efficiency ameliorated the relationship between ACEs and pain intensity (θ_{X → Y|W = 1.75} = -0.53, p = 0.05).

Conclusions: The relationship between ACEs and pain symptomology was comparable to chronic pain phenotypes (i.e., higher pain intensity and pain thresholds) and may vary as a function of brain efficiency in youth. This stresses the importance of assessing for pain symptoms in trauma-exposed youth, as earlier identification and intervention are critical in preventing the chronification of pain.

Significance: This article explores the relationship between ACEs, pain symptomology, and brain efficiency in youth. ACEs may affect how the brain processes information, including pain. Youths with lower brain efficiencies that were exposed to more ACEs have pain symptomology comparable to youths with chronic pain. Understanding this relationship is important for the earlier identification of pain symptoms, particularly in vulnerable populations such as youths exposed to trauma, and is critical for preventing the chronification of pain.

FIGURE 1

Participant Flow Chart. The flow chart shows the progression of participants in the study. ACE‐Q, Adverse Childhood Experiences Questionnaire; CPSS‐V, The Child PTSD Symptom Scale for DSM‐V; MRI, Magnetic resonance imaging; QST, Quantitative sensory testing. *Participants were removed if missing data in at least one variable of interest. Excluded participants may be missing data in more than one variable.

FIGURE 2

Moderating Effect of Global Brain Efficiency on ACEs and Pain Intensity. (a) Johnson‐Neyman plot and confidence bands for the conditional relationship between ACEs and pain intensity as a function of global brain efficiency. (b) Plot visualizing the conditional effects of global efficiency on the relationship between ACEs and pain intensity ratings. Regression slopes for low (≤0.25; p = 0.05), average (0.27; p = 0.93), and high (≥0.28; p = 0.05) global brain efficiencies were plotted over individual data points. This model explains 28% of the total variance. Included in the analysis were N _Baseline  = 44 and N _Follow‐up  = 42. Some data points overlap. N.s., non‐significance.

FIGURE 3

Moderating Effect of Global Brain Efficiency on ACEs and Heat Pain Sensitivity. (a) Johnson‐Neyman plot and confidence bands for the conditional relationship between ACEs and heat pain thresholds as a function of global brain efficiency. (b) Plot visualizing the conditional effects of global efficiency on the relationship between ACEs and heat pain thresholds. Regression slopes for low (≤0.25; p = 0.05) and average (0.27; p = 0.74) global brain efficiencies were plotted over individual data points. This model explains 10% of the total variance. Included in the analysis were N _Baseline  = 44, N _Follow‐up  = 42. Some data points overlap. N.s., non‐significance.