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title

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

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

💻 패턴

Q-learning model fit (drug-bias parameter)

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

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)

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

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

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

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

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

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