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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...

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Predicting cognition using estimated structural and functional connectivity networks and artificial intelligence in

Ceren Tozlu, Dylan Ong, Christopher Piccirillo

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|April 16, 2025

Summary

Artificial intelligence models can predict cognitive function in multiple sclerosis (MS) patients using estimated structural and functional connectomes (eSC and eFC) derived from MRI lesion masks. This approach shows promise for personalized treatment planning.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Prior research established that AI-generated estimated structural and functional connectomes (eSC and eFC) from MS lesion masks predict disability.
Traditional SC and FC derived from diffusion and functional MRI are standard but resource-intensive methods.

Purpose of the Study:

To evaluate the efficacy of eSC and eFC in predicting both baseline and long-term (4-year) cognitive performance in multiple sclerosis (MS) patients.
To explore the potential of AI-driven connectome estimation for clinical applications in MS.

Main Methods:

Estimated structural connectomes (eSC) were generated using the Network Modification tool from clinical MRI-derived lesion masks.
Estimated functional connectomes (eFC) were derived using the Krakencoder AI model, with eSC as input.
Cognitive performance was assessed using the Symbol Digit Modalities Test (SDMT).

Main Results:

The highest prediction accuracy for follow-up SDMT scores was achieved using regional eSC (Spearman's correlation = 0.58) and eFC (Spearman's correlation = 0.56).
These prediction accuracies are comparable or superior to those reported in other studies on healthy and diseased cohorts.
Specific patterns of eSC and eFC alterations, including cerebellar and default mode network changes, were associated with lower cognitive scores.

Conclusions:

Clinically acquired MRI data combined with AI models can generate reliable eSC and eFC for predicting cognitive function in MS.
Lesion-based connectome estimation offers a promising, potentially more accessible method for improving individualized treatment strategies for cognitive impairments in MS.