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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Published on: July 1, 2014

Image complexity-based fMRI-BOLD visual network categorization across visual datasets using topological descriptors

Debanjali Bhattacharya^1,2, Neelam Sinha^3,4, R Yashwanth⁵

¹Department of Artificial Intelligence, Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bengaluru, 560035, India. b_debanjali@blr.amrita.edu.

Scientific Reports

|October 22, 2025

Summary

This summary is machine-generated.

Researchers used topological data analysis of brain activity to differentiate visual networks. This method accurately classifies visual stimuli, offering potential for diagnosing visual processing disorders.

Keywords:

Classification Deep hybrid learning FMRI time-series Partial correlation Topological data analysis Visual network

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

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Last Updated: Jan 14, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Published on: July 1, 2014

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Neuroscience
Data Science
Computer Vision

Background:

Understanding visual perception involves analyzing brain activity patterns.
Functional Magnetic Resonance Imaging (fMRI) Blood-Oxygen-Level-Dependent (BOLD) signals provide insights into neural activity.
Topological Data Analysis (TDA) offers novel methods for characterizing complex data structures.

Purpose of the Study:

To investigate differences in the topological characteristics of visual networks.
To examine how network topology varies in response to distinct visual stimuli (COCO, ImageNet, SUN datasets).
To explore the potential of TDA in classifying visual network responses.

Main Methods:

Constructed visual networks from fMRI BOLD time-series data using the BOLD5000 dataset.
Computed 0- and 1-dimensional persistence diagrams for each visual network.
Applied K-means clustering to extract features from persistence diagrams.
Utilized a novel deep-hybrid model for classification of visual networks.

Main Results:

The deep-hybrid model achieved 90-95% accuracy in classifying visual networks based on topological features.
Distinct topological patterns were identified for visual networks associated with different image datasets.
The study successfully captured differences in BOLD signals corresponding to images of varying complexity and context.

Conclusions:

Topological analysis of fMRI data provides a robust method for categorizing visual network responses.
This approach can differentiate brain activity patterns elicited by diverse visual stimuli.
Findings suggest potential for developing neuroimaging biomarkers for visual processing disorders and cognitive tracking.