# Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality

arXiv: https://arxiv.org/abs/2101.10471 · HTML: https://mlsanity.com/paper/2101.10471/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 80 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Machine Learning for Neuroimaging with Scikit-Learn](https://mlsanity.com/paper/1412.3919/) (2014)
- [MRI Tumor Segmentation with Densely Connected 3D CNN](https://mlsanity.com/paper/1802.02427/) (2018)
- [Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks](https://mlsanity.com/paper/2011.08261/) (2020)
- [A Framework for Exploring Non-Linear Functional Connectivity and Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric Clustering](https://mlsanity.com/paper/1407.3809/) (2014)
- [Online Multivariate Anomaly Detection and Localization for High-dimensional Settings](https://mlsanity.com/paper/1905.07107/) (2019)
- [Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI](https://mlsanity.com/paper/2101.01832/) (2021)
- [Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)](https://mlsanity.com/paper/2101.09354/) (2021)

## Built on by (most cited first, 4 of 4)

- [Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images](https://mlsanity.com/paper/2406.12683/) (2024)
- [Re-defining Radiology Quality Assurance (QA) -- Artificial Intelligence (AI)-Based QA by Restricted Investigation of Unequal Scores (AQUARIUS)](https://mlsanity.com/paper/2205.00629/) (2022)
- [Cross Modal Global Local Representation Learning from Radiology Reports and X-Ray Chest Images](https://mlsanity.com/paper/2301.10951/) (2023)
- [Enhancing Graph Attention Neural Network Performance for Marijuana Consumption Classification through Large-scale Augmented Granger Causality (lsAGC) Analysis of Functional MR Images](https://mlsanity.com/paper/2410.18506/) (2024)

## Abstract (arXiv)

The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. The lsXGC is a multivariate approach since it identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here lsXGC serves as a biomarker for classifying schizophrenia patients from typical controls using a subset of 62 subjects from the Centers of Biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall's tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity. We cross-validate 100 different training/test (90%/10%) data split to obtain mean accuracy and a mean Area Under the receiver operating characteristic Curve (AUC) across all tested numbers of features for lsXGC. Our results demonstrate a mean accuracy range of [0.767, 0.940] and a mean AUC range of [0.861, 0.983] for lsXGC. The result of lsXGC is significantly higher than the results obtained with the cross-correlation, namely mean accuracy of [0.721, 0.751] and mean AUC of [0.744, 0.860]. Our results suggest the applicability of lsXGC as a potential biomarker for schizophrenia.