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Advancements in Predicting Cognitive Abilities Through Multimodal Brain Imaging

Advancements in Predicting Cognitive Abilities Through Multimodal Brain Imaging

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Recent research demonstrates that combining multiple MRI modalities enhances the prediction and reliability of cognitive abilities, offering promising insights into brain health and individual differences.

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Understanding and predicting cognitive abilities using brain imaging technologies has been a long-standing objective in cognitive neuroscience. Recent developments have focused on integrating multiple MRI modalities to enhance the accuracy and reliability of these predictions. While previous studies primarily relied on single MRI types, new research demonstrates that combining structural MRI, resting-state and task-based functional connectivity, along with blood-oxygen-level-dependent (BOLD) responses during task performance, yields a more comprehensive neural marker of cognition.

A study published in PNAS Nexus by Narun Pat and colleagues employed a stacking technique to merge data from different MRI modalities. The study analyzed a large sample of 2,131 participants aged 22 to 100 from datasets in the United States and New Zealand. The results showed that this multimodal approach significantly improved the prediction of cognitive test scores obtained outside the scanner. Moreover, applying this method to the Dunedin Multidisciplinary Health and Development Study revealed that brain imaging at age 45 could predict childhood cognitive scores (ages 7, 9, and 11) with a Pearson's correlation coefficient of 0.52, indicating substantial predictive accuracy.

One of the key advantages of this stacking method was its ability to enhance test-retest reliability, suggesting that it captures stable individual differences in cognitive abilities more effectively than models relying on a single MRI modality. The models maintained performance even when trained on one dataset and tested on an independent dataset, achieving a correlation of 0.25. While this cross-sample generalizability was lower than within-dataset predictions, it still marks progress toward more robust neuroimaging biomarkers of cognition.

This research underscores the potential of multimodal MRI integration in creating reliable and predictive neural markers for cognitive functions, paving the way for better understanding and assessment of individual differences in brain health and cognition.

Source: https://medicalxpress.com/news/2025-06-cognitive-abilities-brain-scans.html

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