# Cortical information integration as a model for pain perception and behavior

> **NIH NIH RF1** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $1,978,210

## Abstract

Sensory processing requires the interaction between external inputs and an internal brain state. Pain is a unique
sensory experience that is triggered by external signals, but is also strongly shaped by internal cognitive and
emotional variables. At the circuit level, there is not a single primary pain cortex; instead, a distributed network
of cortical areas process and regulate pain. For example, the primary somatosensory cortex (S1) is known to
process stimulus-evoked information, such as location and timing. The anterior cingulate cortex (ACC), in
contrast, gives rise to the aversive experience of pain and displays a high level of neuronal plasticity in the
chronic pain state. Meanwhile, the prefrontal cortex (PFC) can strongly modulate pain behaviors. However, the
mechanisms whereby these distributed cortical pain circuits integrate information remain largely unknown.
Thus, we propose a novel conceptual and computational framework for pain as a converging, temporally
specific, interaction among the S1, the ACC, and the PFC. This interaction can be described by a predictive
coding framework that combines feedforward inputs with top-down predictions dependent on prior aversive
experiences, and modulatory commands, based on neural activities in the S1, ACC and PFC. To test this
hypothesis, we will create a new set of tools for pain studies. We will design devices to accurately measure
pain responses; engineer closed-loop brain-computer interfaces (BCIs) to selectively perturb cortical circuits
during the precise time course of pain; and define novel statistical methods such as mechanistic mean-field
models to analyze dynamic cortical information integration, using local field potentials (LFPs) and ensemble
spikes. In Aim 1, we will identify the impact of the nociceptive information on central pain circuit dynamics. We
will characterize the directed information flow between the S1, ACC, and PFC (more specifically the prelimbic
PFC), before and after noxious stimulation. We will create closed-loop BCIs using a real-time pain detection
algorithm based on statistical analyses of simultaneous spikes and LFPs in the S1 and ACC to optogenetically
modulate the S1, and show that such perturbations disrupt the integration of signals from the ACC and PFC to
impact pain behaviors. We will also analyze how chronic pain alters predictive coding schemes and response
to acute pain. In Aim 2, we will use BCIs to test the impact of ACC modulation on neural activities in the S1 and
PFC, as well as on pain behaviors. More importantly, we will show that chronic pain can induce maladaptive
plasticity in the ACC, which in turn alters the information flow from the S1 and PFC to give rise to pain
anticipation and tonic pain – two examples of pain experience driven by an internal aversive state. In Aim 3, we
will show that BCI-driven modulation of PFC outputs can provide scalable regulation of the nociceptive
information flow from the S1 and ACC to alter pain behaviors. ...

## Key facts

- **NIH application ID:** 10205303
- **Project number:** 1RF1NS121776-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Zhe Sage Chen
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,978,210
- **Award type:** 1
- **Project period:** 2021-09-22 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10205303

## Citation

> US National Institutes of Health, RePORTER application 10205303, Cortical information integration as a model for pain perception and behavior (1RF1NS121776-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10205303. Licensed CC0.

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