{'fraction_non_alphanumeric': 0.019471488178025034, 'fraction_numerical': 0.002086230876216968, 'mean_word_length': 5.51131221719457, '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}}
Two models for fusion of medical imaging data: Comparison and connections

Exploitation of complementary information is the principal reason for collecting data from multiple neurological sensors. Since little is known about the latent processes underlying neural function, it is important to minimize the assumptions placed on the data when performing a joint analysis. This motivates the use of data-driven fusion methods, such as independent vector analysis (IVA), for the analysis of neurological data. For neural datasets, the complementary information exploited by fusion methods may be in the form of similar spatial activation across datasets, the spatial IVA (sIVA) model, or similar subject relations across datasets, the transposed IVA (tIVA) model. Despite the potential power of these two models, no study has investigated how the differences in the modeling assumptions of sIVA and tIVA inform the fusion of real neuro-imaging data. In this paper, we utilize a unique set of multitask functional magnetic resonance imaging data from 271 subjects to directly compare the sIVA and tIVA models and visualize their differences using a novel technique, global difference maps. Through this application, we note important similarities between the results from the two methods that increase our confidence in their overall performance, though differences in modeling assumptions result in certain differences in the decompositions.