Editor’s note: This article is
by Dr. Guillermo Cecchi of IBM Research’s Computational Biology Group.
Pain, whether dealing with a healthy or sick person, is an
enormous part of medicine. Yet it is poorly understood. Consider this: we are
often asked to rate our perception of pain intensity, by reporting it in a
scale between 1 and 10. In this way, pain can be measurable. However, pain is a
highly subjective phenomenon, heavily determined by perceptual and cognitive
mechanisms, so that individuals’ pain perception levels vary widely.
Chronic pain affects at least 10 percent of the population
[source:
Harstall C and Ospina M (June 2003). "How
Prevalent Is Chronic Pain?". Pain Clinical Updates,
International Association for the Study of Pain XI (2): 1–4.]
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But there are patterns. I have been working with
with my colleague
Irina Rish, and Northwestern University’s Dr. Apkar Apkarian at his Pain and Emotion Lab for
several years to find those patterns in function Magnetic Resonance Imaging
(fMRI) scans. And this month, our paper
Predictive
Dynamics of Human Pain Perception detailed findings based on
experiments carried out to understand the emergent properties of functional
brain networks shown on these scans.
We demonstrated that subjective responses to pain can be
captured by a unique model, through fMRI, where individual differences are
determined by a handful of parameters. A physician could then use this
knowledge to personalize patient diagnosis and treatment.
Measuring pain
To be clear, none of the study’s 26 participants were
harmed. We measured brain activity based on stimuli delivered via a thermal
plate at different points on the skin. They were told that the plate would
change temperature, but not when or to what temperature.
We describe this in the paper in more details, but the mind
displays three features when in pain:
- “First,
the pain must signal the threat of tissue damage. This is determined by
the current value of the skin temperature. The signal magnitude must consistently
increase with the temperature, although not necessarily linearly (as in
fact, tissue damage is not linear with temperature).
- “Secondly,
this signal magnitude [registered in the brain] must anticipate the
possibility of damage – sounding the alarm of an imminent threat, given
the recent history of temperature values, independently of the current
temperature. This information can also be partially captured by the skin
temperature’s rate of change.
- “Finally,
given its powerful hold on behavior, the intensity of pain perception must
quickly decay once the threat of damage disappears, so as not to interfere
with [a person’s] other ongoing mental states.”
The raw value of the temperature is just one of the drivers
of perception. Our work also showed that there are other components, such as
the need to anticipate potential damage, even when the current temperature is
comfortable, and the need to forget quickly, even when the current temperature
is high, but falling.
Our work represents the first model of
pain as a perception with a predictable, deterministic signature. Beyond
pain, there are very few examples of similar models for other mental
processes, and our work shows the power of simultaneously modeling mental
states and brain activity.
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And the brain activity of our participants reflected these
components.
In fact, the brain areas that can be used to infer the raw
value of the temperature are different from those that are used to infer pain
perception. Moreover, combining the inferred pain perception from fMRI readings
with a prediction based on the inferred temperature gives us the best
predictive model.
Helping doctors help your pain
By showing that subjective responses to pain can be
constrained by a handful of parameters, doctors can personalize patient
diagnosis and treatment. Our “mind reading” approach of using fMRI also implies
that it is possible to infer the level of pain a person is suffering even when
he or she cannot report it verbally, or otherwise. We hope that it will also be
the basis of a more accurate prognosis for those patients with risk of
developing chronic pain (for instance, those with prolonged back pain).
The next steps in this study are to find out how the model
changes for patients with chronic pain, in the hope that it will reveal
specific mechanisms disrupted by the condition. And we will also study
pharmacological effects on the model to answer questions such as: which part of
the model is affected by analgesics and anesthesia?
Labels: Apkar Apkarian, biology, computational biology, PLOS
