Antigravity Agent
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138 lines
4.5 KiB
Markdown
138 lines
4.5 KiB
Markdown
---
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id: wiki-2026-0508-amygdala-hyperactivity
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title: Amygdala Hyperactivity
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Amygdala Hyperreactivity, Limbic Hyperactivation]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [neuroscience, anxiety, ptsd, amygdala]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: nilearn/mne
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---
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# Amygdala Hyperactivity
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## 매 한 줄
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> **"매 amygdala 의 exaggerated response 의 threat / emotional stimuli — 매 anxiety, PTSD, depression 의 transdiagnostic biomarker."** 매 fMRI BOLD response 의 elevation (특히 face/threat tasks) — 매 prefrontal regulation 의 hypoactivity 와 pair — 매 2026 에 SSRI, CBT, TMS, psychedelic-assisted therapy 의 normalization target.
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## 매 핵심
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### 매 circuit
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- **Amygdala**: 매 basolateral (BLA, threat learning) + central (CeA, autonomic output).
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- **vmPFC / dlPFC**: 매 top-down inhibition — 매 hyperactivity 와 anti-correlation.
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- **HPA axis**: 매 amygdala → CRH → cortisol — 매 chronic activation 의 maladaptive.
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### 매 conditions
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- **Anxiety disorders**: GAD, social anxiety, panic.
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- **PTSD**: 매 trauma-associated cue 의 sensitization.
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- **MDD**: 매 sad-face bias.
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- **BPD**: 매 emotional reactivity.
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- **Autism**: 매 mixed — face-processing 의 atypical activation.
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### 매 응용
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1. Diagnostic biomarker (research stage).
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2. Treatment response prediction (SSRI, exposure therapy).
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3. Neurofeedback / TMS target localization.
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## 💻 패턴
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### fMRI BOLD extraction
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```python
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from nilearn import image, masking, datasets
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# Load Harvard-Oxford amygdala mask
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atlas = datasets.fetch_atlas_harvard_oxford('sub-maxprob-thr25-2mm')
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amyg_mask = image.math_img("img == 10", img=atlas.maps) # left amyg label
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# Extract task BOLD
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bold = image.load_img("sub-01_task-faces_bold.nii.gz")
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amyg_ts = masking.apply_mask(bold, amyg_mask).mean(axis=1)
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```
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### Threat > neutral contrast
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```python
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from nilearn.glm.first_level import FirstLevelModel
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events = pd.DataFrame({
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"onset": [0, 20, 40, 60],
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"duration": [10]*4,
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"trial_type": ["threat", "neutral", "threat", "neutral"],
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})
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flm = FirstLevelModel(t_r=2.0, hrf_model="spm")
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flm.fit(bold, events=events)
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contrast = flm.compute_contrast("threat - neutral", output_type="z_score")
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```
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### Functional connectivity (amyg-vmPFC)
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```python
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from nilearn.connectome import ConnectivityMeasure
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# Time series from amyg + vmPFC ROIs
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ts = np.column_stack([amyg_ts, vmpfc_ts])
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conn = ConnectivityMeasure(kind="correlation")
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fc = conn.fit_transform([ts])[0] # 2x2 corr matrix
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amyg_vmpfc_fc = fc[0, 1] # negative in healthy, weaker in anxiety
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```
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### HRV proxy (peripheral readout)
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```python
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import neurokit2 as nk
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ecg = nk.ecg_clean(ecg_signal, sampling_rate=500)
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peaks = nk.ecg_peaks(ecg, sampling_rate=500)[0]
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hrv = nk.hrv_time(peaks, sampling_rate=500)
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# Low RMSSD ↔ high sympathetic ↔ amyg overdrive
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```
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### Real-time fMRI neurofeedback target
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```python
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def neurofeedback_signal(current_volume, amyg_mask, baseline_mean, baseline_std):
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activation = masking.apply_mask(current_volume, amyg_mask).mean()
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z = (activation - baseline_mean) / baseline_std
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# Display inverted bar — patient learns to downregulate
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return -z
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```
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## 매 결정 기준
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| 상황 | Intervention |
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|---|---|
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| Acute anxiety | Benzodiazepine (short-term), breathing |
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| Chronic anxiety | SSRI/SNRI + CBT |
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| PTSD | Trauma-focused CBT, EMDR, prazosin (nightmares) |
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| Treatment-resistant | TMS (dlPFC), ketamine, psilocybin trials |
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| Research / monitoring | fMRI + HRV biomarkers |
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**기본값**: 매 CBT + SSRI — 매 6-12 weeks 의 expected normalization.
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## 🔗 Graph
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- Adjacent: [[Autism-Spectrum-Disorder]]
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## 🤖 LLM 활용
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**언제**: 매 patient psychoeducation, 매 literature summarization.
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**언제 X**: 매 diagnosis, 매 treatment prescription — 매 clinician 의 only.
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## ❌ 안티패턴
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- **Single-region focus**: 매 amygdala alone — 매 circuit (vmPFC, hippocampus) 의 consideration.
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- **State vs trait conflation**: 매 task-induced state ≠ stable trait.
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- **Reverse inference**: 매 amyg activation = "fear" — 매 many functions.
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- **fMRI as diagnostic**: 매 group-level 의 individual 의 X.
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## 🧪 검증 / 중복
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- Verified (Etkin & Wager 2007 meta-analysis, Shin & Liberzon 2010, Stein et al. 2007).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — circuit + biomarker patterns |
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