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PRISM RESEARCH · ECAP
AI Has Destroyed The Barrier
Between Institutions and
Everyday Practitioners.
ECAP stands for Endocannabinoid-Associated Pathways. It is a systems model that looks at how cannabis-related signals may move through brain chemicals, receptors, the stress system, and the immune system. The goal is simple: make the science clear enough for everyday readers, and solid enough for serious research and real-world use.
ECAP also shows something bigger: with AI as a research partner, a single practitioner can produce institutional-grade work. This page is here to bridge the gap between academic science and everyday practitioners, and to invite others to build on the model.
Prefer to go straight to the science? Read the ECAP preprint (DOI) or view it on Academia.edu .
Author
Dr. Jeff Bullock, PharmD
Applied Neuroscience & AI Modeling Researcher
Creator of the ECAP Framework
Status
Open preprint · Creative Commons (CC BY 4.0)
DOI: 10.5281/zenodo.17467525
Focus
ECAP treats the endocannabinoid system as one hub inside a larger network. It maps how cannabis-related signals may move through brain chemicals, receptors, the stress system, and the immune system, so other teams can test, question, and improve the model.
Research tags
In this short video, I walk through why I built ECAP, how it turns complex body science into a clear map, and how we use AI at PRISM as a research assistant—not a replacement for human judgment.
ECAP cover art: a visual hint that many body pathways and signals work together.
What ECAP Is
ECAP treats the endocannabinoid system as one part of a much bigger network. That network includes brain chemicals, receptors, the stress system, and the immune system.
Instead of telling a single story about one receptor or one “entourage effect,” ECAP lays out many possible pathways. It turns those pathways into a research model that other teams can test in real studies.
In simple terms: ECAP is a map. It does not tell you what to believe. It helps you ask better questions about how cannabis-related compounds may affect the brain and body.
Why I Created ECAP
My goal:
Make dense neuroscience understandable without watering it down, and give people a real way to move science forward with AI.
When I looked at cannabis science, I saw two extremes. On one side, there were dense papers that only specialists could follow. On the other side, there were vague claims and simple stories that did not match how the body really works.
I wanted a middle path. I wanted a model that:
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Serious researchers could test, question, and improve.
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Clinicians and thoughtful operators could understand and apply with care.
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Curious readers could follow without needing a PhD.
AI helped me build that model faster. It helped me scan papers, spot patterns, and draw diagrams. But AI did not replace judgment. Every part of ECAP comes from human review, cross-checking, and clear limits on what we can and cannot say yet.
A big part of this work is also a signal: with discipline, curiosity, and the right tools, everyday practitioners can help move science forward. You do not need a giant lab to ask serious questions.
How the ECAP Model Is Organized
ECAP looks at the body in layers. Each layer asks a simple question: “What signals are here, and how might they work together?”
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Receptors: places on cells that “listen” to signals, including classic cannabinoid receptors and related targets.
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Brain chemicals: messengers that affect mood, focus, pain, and sleep.
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Stress system: how the body reacts to pressure, threat, and recovery.
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Immune system: how the body handles injury, inflammation, and repair.
For each layer, ECAP points to patterns. Those patterns become testable questions. The goal is not to promise results. The goal is to guide careful trials and real-world studies.
FIGURE
ECAP Network Map
A high-level map can show ECAP as one network of receptors, brain chemicals, the stress system, and the immune system.
FIGURE
System Coverage Snapshot
This small chart can show how much of the paper focuses on brain chemicals, immune tone, creativity–action–performance ideas, and other body pathways.
FIGURE
Seven Hypotheses at a Glance
A simple visual can list the main ECAP ideas to test, such as effects on pain, anxiety, sleep, memory, neuroinflammation, and brain protection.
FIGURE
AI Workflow Diagram
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This image can show how PRISM uses AI for reading papers, mapping signals, and making clear visuals, while humans stay in charge of the science.
Figure A (example): A chart can show how different ECAP-style plans might change pain scores over time. Real data should come from well-designed studies.
Figure B (example): A chart can track how stress and anxiety change for different groups as they follow ECAP-style plans.
Creativity, Action and Performance —
A Separate Lens
Alongside ECAP, I explore a separate idea: how body signals might relate to states of Creativity, Action, and Performance. I often shorten this to “Creativity, Action, Performance,” but it is not the same as the ECAP name.
This part is my most theoretical work. It suggests ways body pathways might connect to how we start ideas, follow through, and recover. It is meant to guide careful, controlled studies, not to promise performance boosts or quick wins.
On this page, the creativity–action–performance lens is here to spark structured curiosity. It shows where future research might go, once stronger data is in place.
Figure C (example): A “fingerprint” chart can show how a plan might score on creativity, action, recovery, and clarity once data is collected. This is early-stage theory, not a promise of results.
Who ECAP Is For
ECAP is written in clear language, but it is built for serious work.
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Cannabis brands and product teams: use ECAP as a map when thinking about new products, dosing ranges, and claims you can actually test.
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Clinicians and mental health professionals: use ECAP to frame questions about pain, mood, sleep, and cognition when patients use cannabis or related compounds.
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Researchers and students: use ECAP as a starting framework for study design, biomarker work, and mechanistic questions.
If you work at the edge of neuroscience, cannabis science, or clinical practice, ECAP gives you a shared language to move from “interesting idea” to “testable model.”
Scientific Rigor & Method
This section goes a bit deeper for readers who want more detail on receptors, pathways, and methods.
ECAP is shared as an open preprint with a DOI (10.5281/zenodo.17467525). The paper explains how the framework links classical cannabinoid receptors (CB1 and CB2) with other relevant targets, including TRPV1, GPR55, and PPAR receptors. It also describes how these targets interface with major neurotransmitter systems such as dopamine, serotonin, GABA, and glutamate.
The model extends beyond receptors. It incorporates the stress system (including the HPA axis), oxytocin-related pathways, and the gut–brain–immune network. In other words, ECAP treats cannabinoid-related effects as part of a multi-layer network, not a single “on–off” switch.
In the methods, I describe how I used existing literature, basic pharmacology, and AI tools to build and stress-test the framework. AI helped surface papers, cluster ideas, and suggest possible connections. Human review decided what stayed, what was removed, and how each piece should be framed.
To support rigor, the paper includes:
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Over 200 peer-reviewed sources.
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Clear notes on where evidence is strong vs. speculative.
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Falsifiable ideas for pain, mood, sleep, and brain protection.
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Open-access sharing so other teams can inspect, use, and challenge the model.
The result is a research model, not a treatment guide. ECAP is meant to be tested, refined, and extended over time.
What Comes Next
ECAP is one part of a larger PRISM research portfolio that spans biology, cognition, and economics. ECAP is the first piece to be fully written and released.
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ECAP (Endocannabinoid-Associated Pathways): a systems map for cannabis-related signals across the brain and body.
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Attention and Intention (coming soon): a physics-inspired view of how focus and choice might work in humans and AI.
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Abundance Architecture (coming soon): a framework for building economic liberation and community wealth, with a focus on African American communities.
Across all of these, the theme is the same: use AI to amplify human thinking, not replace it. Use clear research models to turn big questions into real experiments, real data, and real change.