Prediction and Causality of functional MRI and synthetic signal using a Zero-Shot Time-Series Foundation Model

• URL: http://arxiv.org/abs/2509.12497v2
• Date: Wed, 17 Sep 2025 10:11:18 GMT
• Title: Prediction and Causality of functional MRI and synthetic signal using a Zero-Shot Time-Series Foundation Model
• Authors: Alessandro Crimi, Andrea Brovelli,
• Abstract summary: We evaluate a foundation model against classical methods for inferring directional interactions from brain activity measured with fMRI in humans.<n>We tested the forecasting ability of the foundation model in both zero-shot and fine-tuned settings, and assessed causality by comparing Granger-like estimates from the model with standard Granger causality.
• Score: 46.186152268413025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time-series forecasting and causal discovery are central in neuroscience, as predicting brain activity and identifying causal relationships between neural populations and circuits can shed light on the mechanisms underlying cognition and disease. With the rise of foundation models, an open question is how they compare to traditional methods for brain signal forecasting and causality analysis, and whether they can be applied in a zero-shot setting. In this work, we evaluate a foundation model against classical methods for inferring directional interactions from spontaneous brain activity measured with functional magnetic resonance imaging (fMRI) in humans. Traditional approaches often rely on Wiener-Granger causality. We tested the forecasting ability of the foundation model in both zero-shot and fine-tuned settings, and assessed causality by comparing Granger-like estimates from the model with standard Granger causality. We validated the approach using synthetic time series generated from ground-truth causal models, including logistic map coupling and Ornstein-Uhlenbeck processes. The foundation model achieved competitive zero-shot forecasting fMRI time series (mean absolute percentage error of 0.55 in controls and 0.27 in patients). Although standard Granger causality did not show clear quantitative differences between models, the foundation model provided a more precise detection of causal interactions. Overall, these findings suggest that foundation models offer versatility, strong zero-shot performance, and potential utility for forecasting and causal discovery in time-series data.

Related papers

• Generating Causal Temporal Interaction Graphs for Counterfactual Validation of Temporal Link Prediction [7.925229590936017]
  We generate causal temporal interaction graphs (CTIGs) with known ground-truth causal structure.<n>To compare causal models, we propose a distance metric based on cross-model predictive error.<n>Our framework provides a foundation for causality benchmarking.
  arXiv  Detail & Related papers  (2026-02-02T14:36:18Z)
• Beyond Curve Fitting: Neuro-Symbolic Agents for Context-Aware Epidemic Forecasting [6.67449278650973]
  We propose a two-agent framework to predict hand, foot and mouth disease.<n>A "event interpreter" processes signals including school schedules, meteorological summaries, and reports.<n>A neuro-symbolic core then combines this with historical case counts to produce calibrated probabilistic forecasts.
  arXiv  Detail & Related papers  (2025-11-28T15:29:26Z)
• Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood [0.0]
  This study introduces an integrated framework for predictive causal inference designed to overcome limitations in conventional single model approaches.<n> Specifically, we combine a Hidden Markov Model for spatial health state estimation with a Multi Task and Multi Graph Convolutional Network (MTGCN) for capturing temporal outcome trajectories.<n>To demonstrate its utility, we focus on clinical domains such as cancer, dementia, Parkinson disease, where treatment effects are challenging to observe directly.
  arXiv  Detail & Related papers  (2025-07-11T03:11:15Z)
• NeuronTune: Towards Self-Guided Spurious Bias Mitigation [26.544938760265136]
  Deep neural networks often develop spurious bias, reliance on correlations between non-essential features and classes for predictions.<n>Existing mitigation approaches typically depend on external annotations of spurious correlations.<n>We propose NeuronTune, a post hoc method that directly intervenes in a model's internal decision process.
  arXiv  Detail & Related papers  (2025-05-29T22:33:00Z)
• Spatial Reasoning with Denoising Models [49.83744014336816]
  We introduce a framework to perform reasoning over sets of continuous variables via denoising generative models.<n>For the first time, that order of generation can successfully be predicted by the denoising network itself.<n>Using these findings, we can increase the accuracy of specific reasoning tasks from 1% to >50%.
  arXiv  Detail & Related papers  (2025-02-28T14:08:30Z)
• Distinguishing Cause from Effect with Causal Velocity Models [3.0523869645673076]
  We develop a method for causal discovery that extends beyond known model classes such as additive or location scale noise.<n>When the score is estimated well, the objective is also useful for detecting model non-identifiability and misspecification.
  arXiv  Detail & Related papers  (2025-02-07T17:50:14Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Evaluating the Adversarial Robustness for Fourier Neural Operators [78.36413169647408]
  Fourier Neural Operator (FNO) was the first to simulate turbulent flow with zero-shot super-resolution. We generate adversarial examples for FNO based on norm-bounded data input perturbations. Our results show that the model's robustness degrades rapidly with increasing perturbation levels.
  arXiv  Detail & Related papers  (2022-04-08T19:19:42Z)
• Deep Recurrent Modelling of Granger Causality with Latent Confounding [0.0]
  We propose a deep learning-based approach to model non-linear Granger causality by directly accounting for latent confounders. We demonstrate the model performance on non-linear time series for which the latent confounder influences the cause and effect with different time lags.
  arXiv  Detail & Related papers  (2022-02-23T03:26:22Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.