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Found 6 papers, 2 papers with code
Towards understanding the nature of direct functional connectivity in visual brain network
In image complexity-specific VBN classification, XGBoost yields average accuracy in the range of 86. 5% to 91. 5% for positively correlated VBN, which is 2% greater than that using negative correlation.
Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI
The proposed approach is employed for classification of a cohort of 50 healthy control (HC) and 50 Mild Cognitive Impairment (MCI), sourced from ADNI dataset.
Image complexity based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning
To achieve this, 0- and 1-dimensional persistence diagrams are computed for each visual network representing COCO, ImageNet, and SUN.
Investigating the changes in BOLD responses during viewing of images with varied complexity: An fMRI time-series based analysis on human vision
The obtained result of this analysis has established a baseline in studying how differently human brain functions while looking into images of diverse complexities.
Spatial encoding of BOLD fMRI time series for categorizing static images across visual datasets: A pilot study on human vision
Subsequently, parallel CNN model is employed that uses combined 2D features for classifying images across COCO, Imagenet and SUN.
Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5
Structural MRI(S-MRI) is one of the most versatile imaging modality that revolutionized the anatomical study of brain in past decades.
