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Connectome-based markers predict the sub-types of frontotemporal dementia

Molecular Psychiatry (2025)Cite this article

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Abstract

Frontotemporal dementia (FTD) presents a complex spectrum of neurodegenerative disorders, encompassing distinct subtypes with varied clinical manifestations. This study investigates alterations in brain module organization associated with FTD subtypes using connectome analysis, aiming to identify potential biomarkers and enhance subtype prediction. Resting-state functional magnetic resonance imaging data were obtained from 41 individuals with behavioral variant frontotemporal dementia (BV-FTD), 32 with semantic variant frontotemporal dementia (SV-FTD), 28 with progressive non-fluent aphasia frontotemporal dementia (PNFA-FTD), and 94 healthy controls. Individual functional brain networks were constructed at the voxel level and binarized based on density thresholds. Modular segregation index (MSI) and participation coefficient (PC) were calculated to assess module integrity and identify regions with altered nodal properties. The relationship between modular measures and clinical scores was examined, and machine learning models were developed for subtype prediction. Both BV-FTD and SV-FTD groups exhibited decreased MSI in the subcortical module (SUB), default mode network (DMN), and ventral attention network (VAN) compared to healthy controls. Additionally, BV-FTD specifically displayed disrupted frontoparietal network (FPN) integrity compared to other FTD subtypes and controls. All FTD subtypes showed increased PC values in the insular region and reduced connections between the insular and VAN/FPN compared to controls. Moreover, significant associations between specific network alterations and clinical variables were observed. Machine learning models utilizing these matrices achieved high performance in differentiating FTD subtypes. This pilot study reveals diverse brain module organization across FTD subtypes, shedding light on both shared and distinct neurobiological underpinnings of the disorder.

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Fig. 1: Data analysis flowchat.
Fig. 2: Differences in module segregation index and intra- and intermodular connections among the four groups.
Fig. 3: PC values and connections across FTD subtypes.
Fig. 4: Correlations between clinical variables and MSI of networks.
Fig. 5: Performance of multi-class classification between subtypes of FTD and HC.

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Data availability

Data utilized in crafting this article were sourced from the FTLDNI databases, accessible at https://cind.ucsf.edu/research/grants/frontotemporal-lobar-degeneration-neuroimaging-initiative-0. It is important to note that while the investigators associated with FTLDNI contributed to the design and execution of the initiative and/or provided data, they did not partake in the analysis or drafting of this report. The code generated for this study are available on request to the corresponding author.

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Acknowledgements

This work was supported by the University of Macau (MYRG2022-00054-FHS and MYRG-GRG2023-00038-FHS-UMDF), and the Macao Science and Technology Development Fund (FDCT 0015/2023/ITP1, FDCT0048/2021/AGJ, and FDCT0020/2019/AMJ).

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Authors and Affiliations

  1. Centre for Cognitive and Brain Sciences, University of Macau, Taipa, 999078, Macau SAR, China

    Xinglin Zeng, Xiaoluan Xia & Zhen Yuan

  2. Faculty of Health Sciences, University of Macau, Taipa, 999078, Macau SAR, China

    Xinglin Zeng, Xiaoluan Xia & Zhen Yuan

  3. School of Life Science and Technology, Xidian University, Xian, 710126, China

    Jiangshan He

  4. School of Psychology, Shanxi Normal University, Xi’an, Shanxi, 030031, China

    Kaixi Zhang & Shiyang Xu

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Contributions

X.Z.: conceptualization, data curation, methodology, software, data analysis, visualization, writing—original draft, writing—review and editing, visualization. J. H.: data analysis, visualization. K. Z.: data curation. S. X.: data analysis. X. X.: visualization. Y.Z.: supervision, funding support, writing and editing.

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Correspondence to Zhen Yuan.

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Competing interests

The authors declare no competing interests.

Ethics

All methods were performed in accordance with the relevant guidelines and regulations. The study protocol and all procedures were approved by the Institutional Review Board of the University of Macau. Approval was also granted by the FTLDNI ethics committee. The FTLDNI project is coordinated by the University of California, San Francisco (UCSF) Memory and Aging Center, and data were obtained through a data use agreement via the LONI Image & Data Archive. All methods were carried out in accordance with the relevant guidelines and regulations.

Informed consent

All participants in the FTLDNI project provided written informed consent for study participation, data collection, and data sharing in accordance with the Declaration of Helsinki. No identifiable images from human participants are included in this article, and therefore no separate consent for publication of images was required.

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Zeng, X., He, J., Zhang, K. et al. Connectome-based markers predict the sub-types of frontotemporal dementia. Mol Psychiatry (2025). https://doi.org/10.1038/s41380-025-03290-9

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