{'fraction_non_alphanumeric': 0.016164584864070537, 'fraction_numerical': 0.002204261572373255, 'mean_word_length': 5.053333333333334, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Directed Transfer Function Analysis of fMRI Data to Investigate Network Dynamics

In this work, we have adapted the directed transfer function (DTF) to fMRI for the analysis of cortical network dynamics. While modern fMRI sequences are capable of sampling at second or sub-second rates, the underlying hemodynamic response limits the true temporal resolution to the order of 6-12 seconds. Therefore, DTF analysis of fMRI is appropriate for characterizing dynamics in brain response which evolves more slowly than the fMRI response, such as those during learning, fatigue and habituation. In such cases, the response to repeated trials will change with time and a summary measure from each trial can be used as input to the DTF analysis because these summary measures are of appropriate sampling rates and are not affected by the sluggishness of the hemodynamic response. As an example, we investigated the dynamic effects of muscle fatigue on the motor network. Specifically, DTF was used as a multivariate measure of the strength and direction of information flow between the various nodes of the network. We found that the primary motor area had a causal influence on the supplementary motor area, pre-motor area and cerebellum, and this influence initially increased with time and diminished towards the end of the experiment, probably as a result of fatigue