chore(brain): ASTRA 성장 자산 동기화 — 기능 인벤토리·growth(약점프로필/학습큐)·일화기억·장기기억·회의록 원문

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+---
+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 |