CTV-MIND: A cortical thickness-volume integrated individualized morphological network model to explore disease progression in temporal lobe epilepsy
Temporal lobe epilepsy (TLE) is a progressive brain network disorder. Elucidating network reorganization and identifying disease progression-associated biomarkers are crucial for understanding pathological mechanisms, quantifying disease burden, and optimizing clinical strategies. This study aimed t...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | NeuroImage: Clinical |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158225001135 |
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| author | Xinyan Liu Jiaqi Han Xiating Zhang Boxuan Wei Lu Xu Qilin Zhou Yuping Wang Yicong Lin Jicong Zhang |
| author_facet | Xinyan Liu Jiaqi Han Xiating Zhang Boxuan Wei Lu Xu Qilin Zhou Yuping Wang Yicong Lin Jicong Zhang |
| author_sort | Xinyan Liu |
| collection | DOAJ |
| description | Temporal lobe epilepsy (TLE) is a progressive brain network disorder. Elucidating network reorganization and identifying disease progression-associated biomarkers are crucial for understanding pathological mechanisms, quantifying disease burden, and optimizing clinical strategies. This study aimed to investigate progressive changes in TLE by constructing a novel individualized morphological brain network based on T1-weighted structural magnetic resonance imaging (MRI). MRI data were collected from 34 postoperative seizure-free TLE patients and 28 age- and sex-matched healthy controls (HC), with patients divided into LONG-TERM and SHORT-TERM groups. Individualized morphological networks were constructed using the Morphometric INverse Divergence (MIND) framework by integrating cortical thickness and volume features (CTV-MIND). Network properties were then calculated and compared across groups to identify features potentially associated with disease progression. Results revealed progressive hub-node reorganization in CTV-MIND networks, with the LONG-TERM group showing increased connectivity in the lesion-side temporal lobe compared to SHORT-TERM and HC groups. The altered network node properties showed a significant correlation with local cortical atrophy. Incorporating identified network features into a machine learning-based brain age prediction model further revealed significantly elevated brain age in TLE. Notably, duration-related brain regions exerted a more significant and specific impact on premature brain aging in TLE than other regional combinations. Thus, prolonged duration may serve as an important contributor to the pathological aging observed in TLE. Our findings could help clinicians better identify abnormal brain trajectories in TLE and have the potential to facilitate the optimization of personalized treatment strategies. |
| format | Article |
| id | doaj-art-690dead2866e4be2ab27833b4e76eea2 |
| institution | Kabale University |
| issn | 2213-1582 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj-art-690dead2866e4be2ab27833b4e76eea22025-08-20T03:34:19ZengElsevierNeuroImage: Clinical2213-15822025-01-014810384310.1016/j.nicl.2025.103843CTV-MIND: A cortical thickness-volume integrated individualized morphological network model to explore disease progression in temporal lobe epilepsyXinyan Liu0Jiaqi Han1Xiating Zhang2Boxuan Wei3Lu Xu4Qilin Zhou5Yuping Wang6Yicong Lin7Jicong Zhang8School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, ChinaDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China; Department of Neurology, the First Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaSchool of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, ChinaSchool of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, ChinaDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China; Department of Neurology, the First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; Beijing Key Laboratory of Neuromodulation, Xuanwu Hospital, Capital Medical University, Beijing, China; Corresponding authors at: Department of Neurology, Xuanwu Hospital, Capital Medical University, No.45 ChangChun Street, XiCheng District, Beijing 100053, China.Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China; Department of Neurology, the First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; Beijing Key Laboratory of Neuromodulation, Xuanwu Hospital, Capital Medical University, Beijing, China; Corresponding authors at: Department of Neurology, Xuanwu Hospital, Capital Medical University, No.45 ChangChun Street, XiCheng District, Beijing 100053, China.School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China; Hefei Innovation Research Institute, Beihang University, Beijing, China; Corresponding author at: School of Biological Science and Medical Engineering, Beihang University, No.37 XueYuan Road, Haidian District, Beijing 100083, ChinaTemporal lobe epilepsy (TLE) is a progressive brain network disorder. Elucidating network reorganization and identifying disease progression-associated biomarkers are crucial for understanding pathological mechanisms, quantifying disease burden, and optimizing clinical strategies. This study aimed to investigate progressive changes in TLE by constructing a novel individualized morphological brain network based on T1-weighted structural magnetic resonance imaging (MRI). MRI data were collected from 34 postoperative seizure-free TLE patients and 28 age- and sex-matched healthy controls (HC), with patients divided into LONG-TERM and SHORT-TERM groups. Individualized morphological networks were constructed using the Morphometric INverse Divergence (MIND) framework by integrating cortical thickness and volume features (CTV-MIND). Network properties were then calculated and compared across groups to identify features potentially associated with disease progression. Results revealed progressive hub-node reorganization in CTV-MIND networks, with the LONG-TERM group showing increased connectivity in the lesion-side temporal lobe compared to SHORT-TERM and HC groups. The altered network node properties showed a significant correlation with local cortical atrophy. Incorporating identified network features into a machine learning-based brain age prediction model further revealed significantly elevated brain age in TLE. Notably, duration-related brain regions exerted a more significant and specific impact on premature brain aging in TLE than other regional combinations. Thus, prolonged duration may serve as an important contributor to the pathological aging observed in TLE. Our findings could help clinicians better identify abnormal brain trajectories in TLE and have the potential to facilitate the optimization of personalized treatment strategies.http://www.sciencedirect.com/science/article/pii/S2213158225001135Brain network reorganizationBrain age predictionCTV-MINDMRITLE |
| spellingShingle | Xinyan Liu Jiaqi Han Xiating Zhang Boxuan Wei Lu Xu Qilin Zhou Yuping Wang Yicong Lin Jicong Zhang CTV-MIND: A cortical thickness-volume integrated individualized morphological network model to explore disease progression in temporal lobe epilepsy NeuroImage: Clinical Brain network reorganization Brain age prediction CTV-MIND MRI TLE |
| title | CTV-MIND: A cortical thickness-volume integrated individualized morphological network model to explore disease progression in temporal lobe epilepsy |
| title_full | CTV-MIND: A cortical thickness-volume integrated individualized morphological network model to explore disease progression in temporal lobe epilepsy |
| title_fullStr | CTV-MIND: A cortical thickness-volume integrated individualized morphological network model to explore disease progression in temporal lobe epilepsy |
| title_full_unstemmed | CTV-MIND: A cortical thickness-volume integrated individualized morphological network model to explore disease progression in temporal lobe epilepsy |
| title_short | CTV-MIND: A cortical thickness-volume integrated individualized morphological network model to explore disease progression in temporal lobe epilepsy |
| title_sort | ctv mind a cortical thickness volume integrated individualized morphological network model to explore disease progression in temporal lobe epilepsy |
| topic | Brain network reorganization Brain age prediction CTV-MIND MRI TLE |
| url | http://www.sciencedirect.com/science/article/pii/S2213158225001135 |
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