Brain Diffusion Transformer for Personalized Neuroscience and Psychiatry
Zhai, R.; Hu, Y.; Zheng, L.; Xiang, S.; Xie, C.; Peng, L.; Banaschewski, T.; Barker, G. J.; Bokde, A. L. W.; Bruehl, R.; Desrivieres, S.; Flor, H.; Garavan, H.; Gowland, P.; Grigis, A.; Heinz, A.; Lemaitre, H.; Martinot, J.-L.; Martinot, M.-L. P.; Artiges, E.; Nees, F.; Orfanos, D. P.; Poustka, L.; Smolka, M. N.; Hohmann, S.; Holz, N.; Vaidya, N.; Whelan, R.; Zhang, Z.; Robinson, L.; Winterer, J.; King, S.; Zhang, Y.; Kebir, H.; Schmidt, U.; Sinclair, J.; Stringaris, A.; Schumann, G.; Bzdok, D.; Walter, H.; Rolls, E. T.; Sahakian, B.; Robbins, T. W.; Feng, J.; Gong, W.; Jia, T.; IMAGEN Consort
Show abstract
Task-fMRI analyses typically focus on localized activation contrasts between stimuli, neglecting the brains dynamic hierarchy. We introduce Brain Diffusion Transformer (Brain-DiT), a deep generative model capturing recurrent processing underlying individualized neurocognitive state transitions via functional networks. Without prior assumptions, Brain-DiT identifies canonical cognitive regions in the brain and reveals replicable subgroups with distinct neural circuits in large cohorts, offering critical clinical insights overlooked by traditional methods: individuals exhibiting negative emotion bias, linked to language-related regions, had a 12-fold higher likelihood of major depression, and those with maladaptive inhibition strategies, associated with overactive medial frontal regions, showed a 9-fold increased risk of alcohol abuse. By bridging cognitive theory and psychiatric applications, Brain-DiT provides a unified analytical paradigm, paving the way for operational personalized medicine in psychiatry.
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