Nigro Salvatore, Filardi Marco, Tafuri Benedetta, Blasi Roberto De, Dell'Abate Maria Teresa, Giugno Alessia, Gnoni Valentina, Milella Giammarco, Urso Daniele, Zecca Chiara, Zoccolella Stefano, Logroscino Giancarlo
Institute of Nanotechnology, National Research Council (CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100 Lecce, Italy; Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, "Pia Fondazione Cardinale G. Panico", Tricase, Lecce, Italy.
Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, "Pia Fondazione Cardinale G. Panico", Tricase, Lecce, Italy; Department of Italian Language, Literature, and Arts in the World. University for Foreigners of Perugia, Perugia, Italy.
Neuroimage Clin. 2025 Apr 5;46:103780. doi: 10.1016/j.nicl.2025.103780.
Network modeling is increasingly used to study brain alterations in neurological disorders. In this study, we apply a novel modeling approach based on the similarity of regional radiomics feature to characterize gray matter network changes in patients with behavioral variant frontotemporal dementia (bvFTD) using MRI data.
In this cross-sectional study, we assessed structural 3 T MRI data from twenty patients with bvFTD and 20 cognitively normal controls. Radiomics features were extracted from T1-weighted MRI based on cortical and subcortical brain segmentation. Similarity in radiomics features between brain regions was used to construct intra-individual structural gray matter networks. Regional mean connectivity strength (RMCS) and region-to-region radiomics similarity were compared between bvFTD patients and controls. Finally, associations between network measures, clinical data, and biological features were explored in bvFTD patients.
Relative to controls, patients with bvFTD showed higher RMCS values in the superior frontal gyrus, right inferior temporal gyrus and right inferior parietal gyrus (FDR-corrected p < 0.05). Patients with bvFTD also showed several edges of increased radiomics similarity in key components of the frontal, temporal, parietal and thalamic pathways compared to controls (FDR-corrected p < 0.05). Network measures in frontotemporal circuits were associated with Mini-Mental State Examination scores and cerebrospinal fluid total-tau protein levels (Spearman r > |0.7|, p < 0.005).
Our study provides new insights into frontotemporal network changes associated with bvFTD, highlighting specific associations between network measures and clinical/biological features. Radiomics feature similarity analysis could represent a useful approach for characterizing brain changes in patients with frontotemporal dementia.
网络建模越来越多地用于研究神经疾病中的脑改变。在本研究中，我们应用一种基于区域放射组学特征相似性的新型建模方法，利用MRI数据来表征行为变异型额颞叶痴呆（bvFTD）患者的灰质网络变化。
在这项横断面研究中，我们评估了20例bvFTD患者和20名认知正常对照的3T结构MRI数据。基于皮质和皮质下脑分割，从T1加权MRI中提取放射组学特征。利用脑区之间放射组学特征的相似性构建个体内部结构灰质网络。比较bvFTD患者和对照之间的区域平均连接强度（RMCS）和区域间放射组学相似性。最后，在bvFTD患者中探索网络测量、临床数据和生物学特征之间的关联。
相对于对照，bvFTD患者在额上回、右侧颞下回和右侧顶下小叶表现出更高的RMCS值（FDR校正p<0.05）。与对照相比，bvFTD患者在额叶、颞叶、顶叶和丘脑通路的关键成分中还表现出几条放射组学相似性增加的边（FDR校正p<0.05）。额颞叶回路中的网络测量与简易精神状态检查得分和脑脊液总tau蛋白水平相关（Spearman r> |0.7|，p<0.005）。
我们的研究为与bvFTD相关的额颞叶网络变化提供了新见解，突出了网络测量与临床/生物学特征之间的特定关联。放射组学特征相似性分析可能是表征额颞叶痴呆患者脑变化的一种有用方法。
