Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia
Introduction: 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 frontotemp...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158225000506 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849694869114585088 |
|---|---|
| author | Salvatore Nigro Marco Filardi Benedetta Tafuri Roberto De Blasi Maria Teresa Dell’Abate Alessia Giugno Valentina Gnoni Giammarco Milella Daniele Urso Chiara Zecca Stefano Zoccolella Giancarlo Logroscino |
| author_facet | Salvatore Nigro Marco Filardi Benedetta Tafuri Roberto De Blasi Maria Teresa Dell’Abate Alessia Giugno Valentina Gnoni Giammarco Milella Daniele Urso Chiara Zecca Stefano Zoccolella Giancarlo Logroscino |
| author_sort | Salvatore Nigro |
| collection | DOAJ |
| description | Introduction: 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. Methods: 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. Results: 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). Conclusions: 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. |
| format | Article |
| id | doaj-art-d5a174e1cfd64f2186d4e49d6802ccae |
| institution | DOAJ |
| issn | 2213-1582 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj-art-d5a174e1cfd64f2186d4e49d6802ccae2025-08-20T03:19:56ZengElsevierNeuroImage: Clinical2213-15822025-01-014610378010.1016/j.nicl.2025.103780Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementiaSalvatore Nigro0Marco Filardi1Benedetta Tafuri2Roberto De Blasi3Maria Teresa Dell’Abate4Alessia Giugno5Valentina Gnoni6Giammarco Milella7Daniele Urso8Chiara Zecca9Stefano Zoccolella10Giancarlo Logroscino11Institute 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; Corresponding author at: 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; Department of Italian Language, Literature, and Arts in the World. University for Foreigners of Perugia, Perugia, ItalyCenter for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, Italy; Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, ItalyDepartment of Radiology, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, ItalyCenter for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, ItalyCenter for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, ItalyCenter for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, ItalyDepartment of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, ItalyCenter for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, Italy; Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, UKCenter for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, ItalyNeurology Unit, San Paolo Hospital, Azienda Sanitaria Locale (ASL) Bari, Bari, ItalyCenter for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, Italy; Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, ItalyIntroduction: 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. Methods: 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. Results: 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). Conclusions: 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.http://www.sciencedirect.com/science/article/pii/S2213158225000506RadiomicsBrain networkbvFTDCognitionCerebrospinal fluid biomarkers |
| spellingShingle | Salvatore Nigro Marco Filardi Benedetta Tafuri Roberto De Blasi Maria Teresa Dell’Abate Alessia Giugno Valentina Gnoni Giammarco Milella Daniele Urso Chiara Zecca Stefano Zoccolella Giancarlo Logroscino Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia NeuroImage: Clinical Radiomics Brain network bvFTD Cognition Cerebrospinal fluid biomarkers |
| title | Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia |
| title_full | Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia |
| title_fullStr | Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia |
| title_full_unstemmed | Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia |
| title_short | Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia |
| title_sort | radiomics feature similarity a novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia |
| topic | Radiomics Brain network bvFTD Cognition Cerebrospinal fluid biomarkers |
| url | http://www.sciencedirect.com/science/article/pii/S2213158225000506 |
| work_keys_str_mv | AT salvatorenigro radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT marcofilardi radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT benedettatafuri radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT robertodeblasi radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT mariateresadellabate radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT alessiagiugno radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT valentinagnoni radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT giammarcomilella radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT danieleurso radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT chiarazecca radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT stefanozoccolella radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia AT giancarlologroscino radiomicsfeaturesimilarityanovelapproachforcharacterizingbrainnetworkchangesinpatientswithbehavioralvariantfrontotemporaldementia |