ArticleSensors (Basel, Switzerland)2025
Multifractal Cascade Modeling Reveals Fundamental Limits of Current Neuroimaging Strategies.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Neuroimaging assumes spatial and temporal uniformity, yet brain activity exhibits a multifractal cascade structure with intermittent bursts and long-range dependencies. We use controlled simulations to test how well standard sampling strategies (random, grid-based, hierarchical; N = 10-2000 sensors) recover statistical properties-mean, variability, burstiness, and fractal dimension-from synthetic multifractal brain fields. Estimation errors deviate substantially from the classical N-1/2 scaling expected under independent sampling. For higher-order statistics like burstiness, error reduction is remarkably flat in log-log space: orders-of-magnitude increases in sensor density yield virtually no improvement. Grid sampling performs best for fractal dimension at high densities; hierarchical sampling is more stable for burstiness. These results indicate that current neuroimaging fundamentally underestimates brain complexity and variability, with major implications for interpreting both healthy and pathological brain function.
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