# Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI

arXiv: https://arxiv.org/abs/2101.01832 · HTML: https://mlsanity.com/paper/2101.01832/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 74 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [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)

## Built on by (most cited first, 6 of 6)

- [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)
- [Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality](https://mlsanity.com/paper/2101.10471/) (2021)
- [Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments](https://mlsanity.com/paper/2106.00842/) (2021)
- [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)

It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We propose large-scale Extended Granger Causality (lsXGC) and investigate whether it can capture such changes using resting-state fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other time-series of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying marijuana users from typical controls using 126 adult subjects with a childhood diagnosis of ADHD from the Addiction Connectome Preprocessed Initiative (ACPI) database. 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, which is typically used in the literature as a standard measure of functional connectivity. Within a cross-validation scheme of 100 different training/test (90%/10%) data splits, we obtain a mean accuracy range of [0.714, 0.985] and a mean Area Under the receiver operating characteristic Curve (AUC) range of [0.779, 0.999] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912] and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC as a potential biomarker for marijuana use.