Image complexity based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning. (arXiv:2311.08417v1 [eess.IV])

This study proposes a new approach that investigates differences in
topological characteristics of visual networks, which are constructed using
fMRI BOLD time-series corresponding to visual datasets of COCO, ImageNet, and
SUN. A publicly available BOLD5000 dataset is utilized that contains fMRI scans
while viewing 5254 images of diverse complexities. The objective of this study
is to examine how network topology differs in response to distinct visual
stimuli from these visual datasets. To achieve this, 0- and 1-dimensional
persistence diagrams are computed for each visual network representing COCO,
ImageNet, and SUN. For extracting suitable features from topological
persistence diagrams, K-means clustering is executed. The extracted K-means
cluster features are fed to a novel deep-hybrid model that yields accuracy in
the range of 90%-95% in classifying these visual networks. To understand
vision, this type of visual network categorization across visual datasets is
important as it captures differences in BOLD signals while perceiving images
with different contexts and complexities. Furthermore, distinctive topological
patterns of visual network associated with each dataset, as revealed from this
study, could potentially lead to the development of future neuroimaging
biomarkers for diagnosing visual processing disorders like visual agnosia or
prosopagnosia, and tracking changes in visual cognition over time.



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