| name | metabolic-quantum-limit-meg |
| description | Metabolic quantum limit methodology for magnetoencephalography (MEG) — combining quantum sensor energy resolution with neural metabolic power to derive fundamental information capacity bounds for brain imaging. |
| category | neuroscience |
| trigger_words | ["quantum limit","MEG","magnetoencephalography","metabolic bound","information capacity","quantum sensor","Planck constant","spatio-temporal trade-off","Nyquist scale","energy resolution limit"] |
Metabolic Quantum Limit to MEG Information Capacity
Paper: arXiv:2511.06401v3 (Physical Review RESEARCH 8, 023267, 2026)
Authors: E. Gkoudinakis, S. Li, I. K. Kominis
Core Insight
Combines the energy resolution limit of magnetic sensing with the metabolic power available to neural currents to derive a technology-independent bound on the information capacity of MEG. The bound factorizes into geometry, metabolism, and Planck's constant.
Key Results
- Maximum Information Rate: ~2.2 Mbit/s for representative human-brain parameters
- Spatial Resolution Limit: ~1 cm — beyond this, denser sampling provides redundant measurements (information-theoretic Nyquist scale)
- Angular Bandwidth: High multipole components are geometrically attenuated below the quantum-limited noise floor
- Spatio-Temporal Trade-off: Noise variance grows linearly with measurement bandwidth, creating a fundamental trade-off
Methodology
Energy Resolution Limit
The energy resolution limit of magnetic sensors defines a minimum detectable energy per unit bandwidth:
ε_min ∝ ℏ (Planck's constant)
Information Capacity Bound
C_max ∝ (Geometry) × (Metabolism) / ℏ
Nyquist Scale for MEG
The information-limited spatial scale (~1 cm) defines the Nyquist scale for MEG — denser spatial sampling beyond this scale yields redundant measurements rather than additional recoverable information.
Spatio-Temporal Trade-off
- Temporal bandwidth ↑ → Noise variance ↑
- Spatial bandwidth ↑ → Signal attenuation ↑
- Optimal operating point balances both
Applications
- Brain Imaging: Quantitative link between fundamental physics and neuroscience
- Sensor Design: Technology-independent bounds for MEG sensor development
- Signal Processing: Optimal sampling strategies respecting information limits
- Clinical: Improved accuracy for preclinical Alzheimer's diagnosis
Trigger Patterns
Use when analyzing MEG systems, quantum sensors for brain imaging, fundamental limits of neural measurement, or spatio-temporal trade-offs in neuroimaging.
