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Sketchpad Series 

Quantifying human neurophysiological variability across timescales

Poster Session E - Monday, March 9, 2026, 2:30 – 4:30 pm PDT, Fairview/Kitsilano Ballroom

Isabel S. Wilson^1,2, Santiago Isaac Flores Alonso^1,2, Parham Fathi Naz^1,2, Ako Sotiroff^1,2, Polina Shahjahan^1,2, Alex I. Wiesman^1,2; ¹Simon Fraser University, ²Institute for Neuroscience and Neurotechnology

Traditionally, noninvasive human brain imaging analyses have aimed to describe the central tendency of brain activity, while seeking to minimize within- and between-session variability. However, there is increasing recognition that, far from being purely noise, variability in the brain’s signaling is essential to cognitive function and is altered in disease. Despite widespread interest in moment-to-moment neuroimaging signal variability, there is little work quantifying the dependence of variability/stability metrics on measurement timescale. We aim to compare how magnetoencephalography (MEG) activity varies across scales—from milliseconds to minutes, days, weeks, and months—and to determine which of any observed timescale dependencies are associated with fluctuations in cognition and mood. Twenty healthy adults will visit our laboratory 4 times in a month, on days 1, 2, 8, and 29. During each visit, we are collecting MEG data at rest and during somatosensory and auditory tasks, as well as measuring cognition (immediate memory, visuospatial abilities, language, attention, and delayed memory) and mood. In this Scratchpad, we present our analytical pipeline and pilot results, including whole-brain maps of timescale-dependency for task-free delta-, theta-, alpha-, beta-, and gamma-band spectral power across 5 participants. Future results will characterize these dependencies across all 20 participants, examine their associations with daily fluctuations in phenotype, and explore other signal features such as functional connectivity networks. These findings will provide a reference for future work on pathological patterns of variability in clinical populations.

Topic Area: METHODS: Neuroimaging