Characterizing global statistical significance of spatiotemporal hot spots in magnetoencephalography/ electroencephalography source space via excursion algorithms.

Identifying brain regions with high differential response under multiple experimental conditions is a fundamental goal of functional imaging. In many studies, regions of interest (ROIs) are not determined a priori but are instead discovered from the data, a process that requires care because of the great potential for false discovery. An additional challenge is that magnetoencephalography/electroencephalography sensor signals are very noisy, and brain source images are usually produced by averaging sensor signals across trials. As a consequence, for a given subject, there is only one source data vector for each condition, making it impossible to apply testing methods such as analysis of variance. We solve these problems in several steps. (1) To obtain within-condition uncertainty, we apply the bootstrap across trials, producing many bootstrap source images. To discover 'hot spots' in space and time that could become ROIs, (2) we find source locations where likelihood ratio statistics take unusually large values. We are not interested in isolated brain locations where a test statistic might happen to be large. Instead, (3) we apply a clustering algorithm to identify sources that are contiguous in space and time where the test statistic takes an 'excursion' above some threshold. Having identified possible spatiotemporal ROIs, (4) we evaluate global statistical significance of ROIs by using a permutation test. After these steps, we check performance via simulation, and then illustrate their application in a magnetoencephalography study of four-direction center-out wrist movement, showing that this approach identifies statistically significant spatiotemporal ROIs in the motor and visual cortices of individual subjects.