---
id: wiki-2026-0508-neurodevelopmental-disorders
title: Neurodevelopmental Disorders
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Neurodevelopmental Disorders, ADHD, ASD, Dyslexia, NDD]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [neuroscience, psychiatry, adhd, autism, dyslexia, dsm5, ai-screening]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack: { language: python, framework: pytorch-scikit-learn }
---

# Neurodevelopmental Disorders

## 매 한 줄
- 신경발달장애(ADHD, ASD, dyslexia)는 DSM-5 기준 발달 초기 발현·지속적 기능 손상이며, AI는 조기 선별·개인화 중재를 보강한다.

## 매 핵심
- **DSM-5-TR 분류**: ADHD(주의력결핍/과잉행동), ASD(사회적 의사소통 + 제한 반복 행동), Specific Learning Disorder(dyslexia/dyscalculia/dysgraphia), Intellectual Disability, Communication Disorders, Motor Disorders.
- **신경생물**: prefrontal-striatal dopamine 회로(ADHD), cortical underconnectivity & social brain network 이상(ASD), left occipito-temporal "visual word form area" 저활성(dyslexia).
- **유전·환경**: heritability ADHD 0.7–0.8, ASD 0.8 이상, dyslexia 0.5–0.7. CNV(16p11.2), SHANK3, FOXP2 등.
- **개입**: 행동 중재(ABA, CBT), 약물(methylphenidate, amphetamine, atomoxetine; ASD에 risperidone/aripiprazole 보조), 학습 중재(Orton-Gillingham).
- **AI 활용**: eye-tracking + ML로 ASD 12–18개월 조기 선별, 음성/언어 분석으로 dyslexia risk score, EEG/fMRI 기반 ADHD 분류.

## 💻 패턴
```python
# ADHD risk score from CPT (Continuous Performance Task) features
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score

# features: omission_errors, commission_errors, rt_mean, rt_std, post_error_slowing
X = np.load("cpt_features.npy")  # (n_subjects, 5)
y = np.load("dsm5_adhd_label.npy")  # 0/1, clinician-rated

clf = GradientBoostingClassifier(n_estimators=200, max_depth=3, random_state=0)
scores = cross_val_score(clf, X, y, cv=5, scoring="roc_auc")
print(f"ADHD ROC-AUC: {scores.mean():.3f} ± {scores.std():.3f}")
```

```python
# ASD early screening: eye-tracking gaze entropy
import numpy as np
from scipy.stats import entropy

def gaze_entropy(fixations_xy, grid=(8, 8)):
    H, _, _ = np.histogram2d(fixations_xy[:, 0], fixations_xy[:, 1], bins=grid)
    p = H.flatten() / H.sum()
    return entropy(p[p > 0])

# ASD 유아: social-scene 응시 시 gaze entropy 낮음(얼굴 회피)
ent = gaze_entropy(fixations_xy)
flag_high_risk = ent < 2.5  # threshold from validated study
```

```python
# Dyslexia: phonological awareness from speech features
import librosa, numpy as np

def phon_features(wav_path):
    y, sr = librosa.load(wav_path, sr=16000)
    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13).mean(axis=1)
    rate = len(librosa.effects.split(y, top_db=30)) / (len(y) / sr)
    return np.concatenate([mfcc, [rate]])
```

```python
# fMRI ROI features for ASD classification (ABIDE-style)
import nilearn.datasets as nd
from nilearn.connectome import ConnectivityMeasure

correlation = ConnectivityMeasure(kind="correlation", vectorize=True)
fc = correlation.fit_transform(timeseries_list)  # (n_subj, n_features)
# downstream: SVM or shallow MLP, AUC ~0.65–0.75 typical
```

```python
# Methylphenidate dose titration: simple PK model
def mph_concentration(dose_mg, t_hr, ka=1.5, ke=0.3, vd=4.0):
    import numpy as np
    return (dose_mg * ka) / (vd * (ka - ke)) * (np.exp(-ke * t_hr) - np.exp(-ka * t_hr))
```

```python
# DSM-5 ADHD criterion checker (≥6 of 9 inattention OR hyperactivity-impulsivity)
def adhd_dsm5(symptoms_inatt: list[bool], symptoms_hyper: list[bool], age: int):
    threshold = 6 if age < 17 else 5
    inatt = sum(symptoms_inatt) >= threshold
    hyper = sum(symptoms_hyper) >= threshold
    return inatt or hyper
```

## 매 결정 기준
- **ASD 조기 선별**: 18–24개월 M-CHAT-R + eye-tracking 보조 → 양성 시 ADOS-2.
- **ADHD 약물 선택**: 자극제 1차(methylphenidate/amphetamine), 비자극제(atomoxetine, guanfacine) 부작용·동반질환 시.
- **Dyslexia 진단**: IQ 정상이면서 reading achievement < 1.5 SD, phonological deficit 확인.
- **AI 도구 사용 한계**: screening 보조용. 단독 진단 도구로 사용 금지(FDA/PMDA 승인 외).

## 🔗 Graph
- 관련: [[Neuroplasticity]], [[Neurorehabilitation-Post-Stroke]]

## 🤖 LLM 활용
- DSM-5 기준 충족 여부 점검(증상 리스트 → 조건 매칭).
- 부모 면담 transcript에서 ADHD 행동 빈도 추출(zero-shot classifier).
- 임상의의 진단 대체 금지: 보조 요약·문헌 검색에 한정.

## ❌ 안티패턴
- 단일 EEG/fMRI feature로 진단 라벨 출력(false positive 다발).
- 약물 권고를 LLM이 직접 생성(처방권 위반).
- AI screening 양성을 부모에게 "확진"으로 전달.

## 🧪 검증
- 진단 도구: M-CHAT-R(ASD), Conners-3(ADHD), CTOPP-2(dyslexia)와의 일치도(κ ≥ 0.6).
- 약물 반응: 4–6주 follow-up Conners 점수 ≥ 25% 감소.

## 🕓 Changelog
- 2026-05-08 Phase 1: 초안 자동 생성.
- 2026-05-10 Manual cleanup: 본문 보강(150–250 lines), 코드 패턴 6, DSM-5-TR 반영, AI 한계 명시.