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A replicable and generalizable neuroimaging-based indicator of pain sensitivity across individuals

Poster No:

1072

Submission Type:

Abstract Submission

Authors:

Libo Zhang¹, XUEJING LU¹

Institutions:

¹Institute of Psychology, Chinese Academy of Sciences, Beijing, China

First Author:

Libo Zhang
Institute of Psychology, Chinese Academy of Sciences
Beijing, China

Co-Author:

XUEJING LU
Institute of Psychology, Chinese Academy of Sciences
Beijing, China

Introduction:

Pain sensitivity varies widely across individuals. Can we find a reliable index for this variability in pain sensitivity? Identifying it would be important for objectively measuring pain sensitivity, and thereby screening high-risk individuals for chronic pain. Previous studies have attempted to find neural indicators of pain sensitivity with fMRI, but it is still controversial whether pain-evoked brain activations can reflect interindividual pain sensitivity, and whether pain-evoked brain responses selectively track pain sensitivity rather than modality-general stimulus factors. To address these issues, we used six large fMRI datasets and aimed to answer four key questions: (1) Do pain-evoked fMRI responses index variability in pain sensitivity? (2) If so, is this index selective to pain? (3) Can we develop a machine learning model accurately predicting pain sensitivity? (4) Which sample size is needed to index pain sensitivity using pain-evoked fMRI responses?

Methods:

We used six large fMRI datasets (total N=1046) from previous studies where subjects received transient nociceptive stimuli: laser heat in Datasets 1,2, and 5, mechanical pain in Dataset 3, contact heat in Datasets 4 and 6. In Datasets 1&2, subjects also received transient tactile, auditory, and visual stimuli; in Dataset 3&6, subjects also received pain treatments (placebo in Dataset 3, and transcutaneous electric nerve stimulation in Dataset 6). We first examined whether pain-evoked fMRI activity indexes pain sensitivity in Datasets 1~3. We then examined in Datasets 1&2 whether this index is selective to pain by relating tactile, auditory, and visual stimuli-evoked fMRI responses with the corresponding sensory sensitivity. We next developed a machine learning model to predict pain sensitivity using Datasets 1&2, and tested its generalizability to different types of pain stimuli in healthy individuals (Datasets 3&4) and postherpetic neuralgia patients (Dataset 5), and transferability to predicting pain relief in healthy individuals (Datasets 3&6).

·Figure 1. Study overview

Results:

We found that, given a large sample size, pain-evoked fMRI responses reliably correlated with pain sensitivity across individuals. Importantly, this correlation was replicated in multiple independent datasets, and generalized to different types of pain stimuli, namely laser heat, contact heat, and mechanical pains. Furthermore, fMRI responses indexed pain sensitivity better than tactile, auditory, and visual sensitivity, although significant correlations with across-subject perceptual variability could also be observed in non-pain modalities. The machine learning model that we developed (neuroimaging-based indicator of pain sensitivity [NIPS]) could significantly predict not only pain sensitivity to laser heat, contact heat, and mechanical stimuli in healthy individuals and postherpetic neuralgia patients, but also pain relief from different treatments, including placebo and transcutaneous electric nerve stimulation. Notably, a sample size >150 healthy volunteers was required for machine learning models to robustly predict across-subject pain variability, and a sample size of ~200 healthy volunteers was needed to detect univariate correlations between fMRI responses and pain sensitivity with sufficient statistical power.

·Figure 2. Development of NIPS and its performance

Conclusions:

We demonstrated that when large sample sizes are considered, pain-evoked fMRI responses can reflect pain sensitivity across individuals, and pain-evoked fMRI responses contain more information about pain sensitivity than non-pain fMRI responses about sensory sensitivity in non-pain modalities. Our model, NIPS, is versatile enough to predict pain sensitivity and pain relief.

Modeling and Analysis Methods:

Activation (eg. BOLD task-fMRI) ¹

Classification and Predictive Modeling

Perception, Attention and Motor Behavior:

Perception: Pain and Visceral ²

Keywords:

MRI

Pain

^1|2Indicates the priority used for review

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Please indicate below if your study was a "resting state" or "task-activation” study.

Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI

Behavior

For human MRI, what field strength scanner do you use?

3.0T

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SPM

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