Investigating the changes in BOLD responses during viewing of images with varied complexity: An fMRI time-series based analysis on human vision

TL;DR

This study analyzes how human brain responses, measured via fMRI, vary when viewing images of different complexities, using machine learning to classify and segment brain activity patterns from a large dataset.

Contribution

It introduces a novel approach combining classical and deep learning methods to classify and segment fMRI time series based on image complexity, establishing a baseline for vision-related brain activity analysis.

Findings

Successful classification of fMRI signals by image complexity

Temporal segmentation of brain responses during image viewing

Baseline establishment for complexity-dependent brain activity patterns

Abstract

Functional MRI (fMRI) is widely used to examine brain functionality by detecting alteration in oxygenated blood flow that arises with brain activity. This work aims to investigate the neurological variation of human brain responses during viewing of images with varied complexity using fMRI time series (TS) analysis. Publicly available BOLD5000 dataset is used for this purpose which contains fMRI scans while viewing 5254 distinct images of diverse categories, drawn from three standard computer vision datasets: COCO, Imagenet and SUN. To understand vision, it is important to study how brain functions while looking at images of diverse complexities. Our first study employs classical machine learning and deep learning strategies to classify image complexity-specific fMRI TS, represents instances when images from COCO, Imagenet and SUN datasets are seen. The implementation of this classification across visual datasets holds great significance, as it provides valuable insights into the fluctuations in BOLD signals when perceiving images of varying complexities. Subsequently, temporal semantic segmentation is also performed on whole fMRI TS to segment these time instances. The obtained result of this analysis has established a baseline in studying how differently human brain functions while looking into images of diverse complexities. Therefore, accurate identification and distinguishing of variations in BOLD signals from fMRI TS data serves as a critical initial step in vision studies, providing insightful explanations for how static images with diverse complexities are perceived.