2nd/10_Wiki/Topics/Other/Neuroplasticity in Addiction.md

---
id: wiki-2026-0508-neuroplasticity-in-addiction
title: Neuroplasticity in Addiction
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Addiction Plasticity, Reward Learning Plasticity, Drug-Induced LTP]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [neuroplasticity, addiction, dopamine, ltp, mesolimbic]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: brian2-rl
---

# Neuroplasticity in Addiction

## 매 한 줄
> **"매 reward 의 hijack — 매 mesolimbic LTP 의 maladaptive learning"**. 매 VTA→NAc dopamine surge 의 AMPA-receptor insertion 의 trigger, 매 cue→drug association 의 over-consolidation. 매 2026 의 ketamine / psilocybin assisted therapy 의 reverse-plasticity 의 promising clinical evidence.

## 매 핵심

### 매 circuits
- **Mesolimbic (VTA→NAc)**: 매 reward prediction error → 매 reinforcement.
- **Mesocortical (VTA→mPFC)**: 매 craving, executive control 의 erode.
- **Amygdala→NAc**: 매 cue-conditioning, withdrawal-anxiety.
- **Hippocampus→NAc**: 매 contextual cues.

### 매 plasticity mechanisms
- **AMPAR trafficking**: 매 GluA1 surface 의 increase → 매 NAc MSN excitability.
- **Silent synapses**: 매 NMDAR-only 의 cocaine 후 의 unsilencing.
- **Dendritic spines**: 매 stimulants → 매 spine density 의 increase. 매 opioids → 매 decrease.
- **Epigenetic** (ΔFosB, HDAC5): 매 long-term gene-expression 의 lock-in.

### 매 응용
1. 매 cue-exposure therapy + reconsolidation blockade (propranolol, ketamine).
2. 매 TMS / DBS (NAc, sgACC) 의 craving reduction.
3. 매 contingency management + digital phenotyping.

## 💻 패턴

### Q-learning model fit (drug-bias parameter)
```python
import numpy as np
def q_learn_ll(choices, rewards, alpha=0.3, beta=5.0):
    Q = np.zeros(2); ll = 0.0
    for c, r in zip(choices, rewards):
        p = np.exp(beta*Q) / np.exp(beta*Q).sum()
        ll += np.log(p[c] + 1e-9)
        Q[c] += alpha * (r - Q[c])
    return ll
```

### Reconsolidation window detector
```python
from datetime import timedelta
def in_reconsolidation_window(cue_t, now_t, win_min=10, win_max=60):
    dt = (now_t - cue_t).total_seconds() / 60
    return win_min <= dt <= win_max
```

### Striatal MSN STDP (Brian2)
```python
from brian2 import *
G = NeuronGroup(100, 'dv/dt=(El-v)/tau:volt', threshold='v>-50*mV', reset='v=El')
S = Synapses(G, G,
    '''w:1
       dApre/dt=-Apre/tauPre:1 (event-driven)
       dApost/dt=-Apost/tauPost:1 (event-driven)''',
    on_pre='Apre+=dApre; w=clip(w+Apost,0,wmax)',
    on_post='Apost+=dApost; w=clip(w+Apre,0,wmax)')
```

### Cue-reactivity fMRI ROI extraction
```python
from nilearn import input_data
masker = input_data.NiftiMasker(mask_img='nac_left.nii.gz', standardize=True)
ts = masker.fit_transform('subject_task.nii.gz')
craving_corr = np.corrcoef(beta_drug_cue_per_subj, vas_craving)[0, 1]
```

### Digital-phenotyping relapse-risk score
```python
def relapse_risk(z):
    # z: dict of z-scored features (gps_entropy, sleep_var, screen_night, hrv)
    s = 0.4*z['gps_entropy'] + 0.3*z['sleep_var'] \
      + 0.2*z['screen_night'] - 0.1*z['hrv']
    return 1 / (1 + np.exp(-s))
```

### Ketamine plasticity-window dosing protocol stub
```python
from datetime import timedelta
class KetamineProtocol:
    window_h = 24  # BDNF / mTOR peak
    def schedule_therapy(self, infusion_t):
        return infusion_t + timedelta(hours=2)
```

### TMS dlPFC craving protocol
```python
def tms_session():
    return dict(target='left_dlPFC', frequency_hz=10,
                trains=20, pulses_per_train=50,
                inter_train_s=20, intensity_pct_rmt=110)
```

## 매 결정 기준
| 상황 | Intervention |
|---|---|
| Acute craving | TMS dlPFC 10 Hz |
| Treatment-resistant | DBS NAc (case-by-case) |
| Comorbid depression | Ketamine + therapy |
| Stimulant-use disorder | Contingency management + counseling |
| Opioid-use disorder | Buprenorphine + therapy |

**기본값**: CBT + medication + digital tools — 매 multimodal 의 best evidence.

## 🔗 Graph
- 부모: [[Neuroplasticity]] · [[Addiction Neuroscience]]
- 변형: [[Reward Prediction Error]]
- Adjacent: [[Mesolimbic-Pathway]] · [[Dopamine-System]]

## 🤖 LLM 활용
**언제**: 매 mechanism teaching, 매 protocol scaffold, 매 patient-education content.
**언제 X**: 매 clinical decision making — 매 licensed clinician 의 require.

## ❌ 안티패턴
- **Plasticity = bad**: 매 plasticity itself 의 healing 의 vehicle.
- **Single-receptor focus**: 매 D2-only blockade 의 outcomes 의 weak. 매 circuit-level 의 think.
- **Reconsolidation hype**: 매 window narrow, 매 boundary conditions 의 strict.

## 🧪 검증 / 중복
- Verified (Lüscher & Malenka 2011 *Neuron*; Kalivas & Volkow 2005).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — circuits + reversal-therapy patterns |