# MCI Detection using fMRI time series embeddings of Recurrence plots

arXiv: https://arxiv.org/abs/2311.18265 · HTML: https://mlsanity.com/paper/2311.18265/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [Classification of Time-Series Images Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1710.00886/) (2017)
- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (2022)

## Built on by (most cited first, 3 of 3)

- [Leveraging Persistent Homology for Differential Diagnosis of Mild Cognitive Impairment](https://mlsanity.com/paper/2408.15647/) (2024)
- [Non-linear Analysis Based ECG Classification of Cardiovascular Disorders](https://mlsanity.com/paper/2408.01542/) (2024)
- [Pixel-Wise Multimodal Contrastive Learning for Remote Sensing Images](https://mlsanity.com/paper/2601.04127/) (2026)

## Abstract (arXiv)

The human brain can be conceptualized as a dynamical system. Utilizing resting state fMRI time series imaging, we can study the underlying dynamics at ear-marked Regions of Interest (ROIs) to understand structure or lack thereof. This differential behavior could be key to understanding the neurodegeneration and also to classify between healthy and Mild Cognitive Impairment (MCI) subjects. In this study, we consider 6 brain networks spanning over 160 ROIs derived from Dosenbach template, where each network consists of 25-30 ROIs. Recurrence plot, extensively used to understand evolution of time series, is employed. Representative time series at each ROI is converted to its corresponding recurrence plot visualization, which is subsequently condensed to low-dimensional feature embeddings through Autoencoders. The performance of the proposed method is shown on fMRI volumes of 100 subjects (balanced data), taken from publicly available ADNI dataset. Results obtained show peak classification accuracy of 93% among the 6 brain networks, mean accuracy of 89.3% thereby illustrating promise in the proposed approach.