A research team led by Yuan Zhen, head of the Centre for Cognitive and Brain Sciences (CCBS) and professor in the Faculty of Health Sciences (FHS) at the University of Macau (UM), has unveiled for the first time the significant differences in brain network organisation between different subtypes of frontotemporal dementia (FTD). The team has also identified connectome-based neuroimaging biomarkers that can predict and distinguish between the different FTD subtypes, providing new neuroimaging evidence for the precise diagnosis of this neurodegenerative disorder. The research has been published in the prestigious international journal Molecular Psychiatry.
FTD is one of the most common forms of early-onset dementia and primarily comprises three clinical subtypes—behavioural variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA), and nonfluent variant primary progressive aphasia (nfvPPA). While each subtype exhibits distinct behavioural, language, and cognitive symptoms, the underlying brain network disruptions remain poorly understood. In this study, the research team analysed resting-state functional MRI data from over 190 participants, including healthy controls and patients with the three major FTD subtypes, to construct high-resolution voxel-wise connectome framework and investigate modular brain organisation across the different subtypes. The team then used machine learning models to classify the FTD subtypes based on these connectome-derived features.
The results revealed that both bvFTD and svPPA groups exhibited decreased modular segregation index (MSI) in the subcortical module, default mode network, and ventral attention network, while patients with bvFTD displayed specific damage in the frontoparietal network, a system crucial for goal-directed behaviour and executive control. By contrast, patients with nfvPPA exhibited relatively preserved modular segregation, which is consistent with their more localised language impairment. It is also notable that network alterations in the insular and orbitofrontal cortex were strongly linked to patients’ emotional and behavioural abnormalities.
This study demonstrates that connectome-based neuroimaging biomarkers can provide important evidence for the early diagnosis and classification of FTD subtypes, which holds significant importance for advancing precision medicine research on neurodegenerative diseases. The findings also shed light on the shared and distinct neural mechanisms underlying the different FTD subtypes, potentially informing future intervention and treatment strategies.
The corresponding author of this study is Prof Yuan. The first author is Zeng Xinglin, doctoral graduate of UM CCBS and assistant professor in the School of Medicine at the University of Maryland. The research project was funded by the University of Macau (File Nos.: MYRG2022-00054-FHS and MYRG-GRG2023-00038-FHS-UMDF) and the Science and Technology Development Fund of the Macao SAR (File Nos.: 0015/2023/ITP1, 0048/2021/AGJ, and 0020/2019/AMJ). The full text of the research article is available at: https://www.nature.com/articles/s41380-025-03290-9.
| Source: Institute of Collaborative Innovation | |
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